Changelog

We’ve eliminated another manual step from your review workflow. Previously, users had to start Mini Data Extraction (Mini DE) AI manually. EasySLR can now initiate Mini DE AI automatically as soon as an eligible article file or PDF becomes available.

This update helps reviewers access extracted study information earlier, make better-informed screening decisions, and move articles through the review process more efficiently.

What’s new?

When a Mini DE form is configured and AI is enabled, EasySLR automatically runs Mini DE AI in either of the following situations:

• An article file is uploaded during the Title and Abstract (TiAb) screening stage.

• An article moves from the TiAb stage to the Full-Text (FT) stage and has an attached PDF.

Once triggered, the AI reads the available document, identifies relevant information, and populates the configured Mini DE fields without requiring the reviewer to start the process manually.

Before this update

Reviewers had to:

1. Open an eligible article.

2. Locate the Mini DE section.

3. Start Mini DE AI manually.

4. Wait for the extraction to complete.

5. Review the populated values.

With automatic Mini DE AI

Reviewers only need to:

1. Configure the Mini DE form for the project.

2. Enable AI.

3. Upload an article file at TiAb or move an article to the FT stage.

4. Review and validate the automatically extracted values.

Requirements

Automatic Mini DE AI runs only when:

• A Mini DE form has been set up for the project.

• AI is enabled for the Mini DE form.

• A supported article file or PDF is available.

• The article is uploaded at TiAb or progresses from TiAb to FT and PDF is available.

If these conditions are not met, automatic extraction will not begin.

Key benefits

• Fewer manual steps: No need to start Mini DE AI manually.

• Earlier access to information: Relevant study details are extracted as soon as the document becomes available.

• Faster screening: Reviewers can use extracted values to support TiAb and FT decisions.

• Improved consistency: Mini DE AI is triggered systematically across eligible articles.

• Greater efficiency at scale: Teams processing large article volumes can reduce repetitive work.

• Reviewer control: Extracted values remain available for review, validation, and editing.

What do users need to do?

No additional action is required once the Mini DE form is configured and AI is enabled. Continue uploading article files and progressing citations through the workflow as usual. EasySLR will handle the Mini DE AI trigger automatically.

EasySLR now supports report downloads in PowerPoint (PPT) format, alongside the already available comprehensive Word report.

While the Word report provides detailed documentation of your systematic literature review, the new PPT report offers a shorter, more concise version containing the key project information and findings. It is designed for presentations, internal updates, stakeholder meetings, and quick review.

Two report formats for different needs

Word Report- Comprehensive Documentation

The existing Word report provides a detailed account of the review, making it suitable for complete documentation, in-depth review, and further report development.

PowerPoint Report- Concise Presentation

The new PPT report summarises the most important content from the Word report in a clear, slide-based format. It helps teams communicate findings without manually converting a detailed document into a presentation.

Key information covered

Depending on the information available in your EasySLR project, the PPT report may include:

• Project and review overview

• Research objectives and methodology

• Search strategy summary

• Citation and screening progress

• Study selection information

• Included-study overview

• Data extraction highlights

• Quality assessment summary

• Key findings and conclusions

• Relevant tables, charts, and visual summaries

Key benefits

• Save preparation time: Generate a presentation without manually transferring content from the Word report.

• Focus on key findings: Present a concise overview without the extensive detail contained in the full report.

• Communicate clearly: Use a structured, slide-based format that is easier to discuss in meetings.

• Support different audiences: Share the detailed Word report with research teams and the summarised PPT with decision-makers and stakeholders.

• Customise after downloading: Edit the presentation to add branding, commentary, or project-specific context.

• Keep reporting consistent: Generate both outputs from the same EasySLR project information.

How to download the PPT report

1. Open the relevant project in EasySLR.

2. Navigate to the Report Generation section.

3. Generate the report, select Word + Powerpoint option.

4. Download and customize the presentation as needed.

Note: You can also generate PPT for already generated reports.

With this update, you can choose the format that best fits your purpose: download the Word report for comprehensive documentation and the PowerPoint report for a concise, presentation-ready overview.

We’ve enhanced the Tags filter in EasySLR by introducing AND and OR filtering modes. This provides greater control over how articles are identified when multiple tags are selected.

What’s new?

Users can now choose between AND and OR logic when applying multiple tags:

• AND mode: Displays only articles containing all selected tags.

• OR mode: Displays articles containing at least one of the selected tags.

How it works

For example, if the tags RCT, Adult Population, and Diabetes are selected:

• With AND mode, EasySLR displays only articles tagged with all three: RCT, Adult Population, and Diabetes.

• With OR mode, EasySLR displays articles containing any one or more of the three selected tags.

Key capabilities

• Select multiple tags within the Tags filter.

• Choose the most appropriate filter mode-AND or OR.

• Combine tag filtering with other article and screening filters.

• Refine article lists without manually reviewing individual records.

• Quickly create precise or broader article groups based on review requirements.

Benefits

More precise filtering
Use AND mode to identify articles that meet multiple tagging conditions simultaneously.

Broader discovery
Use OR mode to find articles associated with any of the selected tags.

Greater workflow flexibility
Switch between strict and inclusive filtering depending on the review task.

Faster article management
Locate relevant groups of articles more efficiently across large projects.

Improved analysis
Use tag combinations to explore article groups, study characteristics, or review categories.

Impact

This enhancement extends the existing Tags filter and does not change previously assigned tags. Users can continue filtering by a single tag as before or use AND/OR logic when selecting multiple tags.

EasySLR’s version number is now displayed within Account Settings, making it easier to identify, reference, and report the exact application version in use.

What’s new

• Added the current EasySLR version number to the Account Settings page.

• Positioned the information in an accessible location without disrupting existing account controls.

• Standardized the version display for easier reference during support, documentation, and research reporting.

Citation and submission support

The displayed version number can be used when citing EasySLR or documenting the exact version used in research and other submissions. Where required, users can include it in:

• Manuscripts and journal submissions

• Systematic review reports and protocols

• Conference abstracts and presentations

• Theses and dissertations

• Regulatory or institutional documentation

• Supplementary materials and methodology sections

Recording the version helps reviewers and readers understand which release of EasySLR was used to conduct the work, supporting transparency and reproducibility.

How to find it

1. Sign in to EasySLR.

2. Open Account Settings.

3. Locate the EasySLR Version information.

4. Include the displayed version when citing EasySLR or reporting in a submission.

5. Access the Changelog to review the features and enhancements included in that release.

Version-number format

EasySLR versions follow the Year.Month.Date format: YYYY.MM.DD

For example, version 2026.08.31 indicates a release dated 31 August 2026. This date-based format makes it easy to understand when a version was released without consulting separate release documentation.

Benefits

• Quickly confirm the EasySLR version in use.

• Cite the correct version in publications and submissions.

• Maintain clearer records for research reproducibility and audit purposes.

• Include precise version details in bug reports and support requests.

• Verify that users are working with the expected release.

Impact

This is an informational interface enhancement. It does not change existing account settings, permissions, workflows, or user data. No action is required unless users need to record the version for citation, documentation, or submission purposes.

We’ve enhanced the Decision filter in EasySLR to provide a more streamlined and efficient filtering experience across both Title and Abstract (TiAb) and Full-Text (FT) screening stages.

What’s new?

Previously, users could filter articles only by their assigned Exclude Reasons. With this major update, articles can now also be filtered by their assigned Include Reasons.

We have also consolidated the previous Exclude Reasons filter into a single, unified Decision filter. Reviewers and project owners can now access the following options in one place:

• Screening decisions

• Inclusion Reasons

• Exclusion Reasons

The unified filter is available for both the TiAb and FT decisions, providing a consistent experience throughout the review workflow.

A major improvement for quality control

This update is particularly valuable during QC. Project owners can quickly find and review all articles assigned a specific Include or Exclude Reason, without manually checking individual records.

This makes it easier to:

• Confirm that screening criteria have been interpreted correctly

• Identify inconsistent or incorrectly assigned reasons

• Review records associated with a particular decision

• Maintain consistency across reviewers and screening stages

• Focus QC efforts on specific article groups

Key improvements

Filter articles by Inclusion Reasons

Users can now instantly identify all articles assigned a particular Include Reason—functionality that was not previously available.

One unified Decision filter

Screening decisions, Inclusion Reasons, and Exclusion Reasons are now grouped within a single filter, eliminating the need to navigate between separate filtering options.

Available at both screening stages

The enhanced filter can be used for both TiAb and FT decisions, enabling teams to apply the same approach throughout the review.

Faster article identification

Quickly locate articles associated with a specific decision or reason without reviewing records individually.

More efficient and targeted QC

Project owners can group articles by their assigned reasons and perform focused QC on the records most relevant to their review objectives.

Greater screening oversight

Reviewers and project managers gain a clearer view of how articles have been classified, making it easier to identify records that may need reassessment.

How it works

1. Navigate to the relevant article list at the TiAb or FT screening stage.

2. Click on the Decision filter.

3. Select a screening decision, Include Reason, or Exclude Reason.

4. Apply the filter to display articles matching the selected option.

Why this matters

As projects progress, manually locating articles assigned a particular screening reason can be time-consuming and inefficient. Previously, filtering was limited to Exclusion Reasons, making it difficult to review included articles associated with a specific rationale.

By adding Inclusion Reason filtering and combining all screening decisions and reasons into one unified filter, EasySLR enables teams to perform faster, more focused QC, validate classifications, identify inconsistencies, and maintain methodological rigour throughout the review process.

We’ve enhanced background job tracking in EasySLR, making it easier to see which tasks are currently running and access their details.

The taskbar on the screen now displays active background jobs, providing a central view of ongoing processes. Users can also click an active background job to navigate directly to the relevant task details.

What’s new?

Previously, background jobs continued running without providing users with an easy way to track their progress, requiring them to navigate to the relevant page for updates. With this enhancement, users can monitor active jobs directly from the taskbar and click the associated notifications to view more information, without leaving their current screen.

Key improvements

• View active tasks from the taskbar
  The taskbar displays background jobs that are currently running, giving users a clear overview of ongoing activity.

• Access task details directly
  Click an active background job notification to open the relevant details and better understand what is being processed.

• Track long-running processes
  Monitor tasks that may take additional time to complete without needing to remain on the page where they were initiated.

• Check existing jobs before starting another
  Quickly confirm whether a task is already in progress before initiating the same or a similar action.

• Navigate more efficiently
  Instead of searching through different sections of a project, users can access information about an active task directly from its notification.

How it works

When an EasySLR activity runs in the background:

1. The task appears in the taskbar as an active background job.

2. A progress bar indicates that the process is currently running.

3. Users can click the active task to view the relevant details.

Why this matters

Some EasySLR tasks continue running in the background and may take time to complete. Previously, users might have been unsure whether a task was still processing, completed, or needed to be initiated again.

The enhanced taskbar provides greater transparency into background activity. Users can quickly see what is running, access the relevant details, and track progress.

This update creates a clearer and more efficient experience for managing ongoing tasks across EasySLR projects.

We’ve introduced new AI-powered filters for Mini Data Extraction (Mini DE), making it easier for reviewers and project owners to identify and filter articles based on AI-extracted Mini DE data.

What’s New?

The Mini DE filters now allow users to filter articles based on the values extracted by AI for configured Mini DE fields.

This provides better visibility into AI-extracted data and makes it easier to identify specific groups of articles.

Key Capabilities

• Filter articles using Mini DE AI data
  Filter articles based on values extracted by AI in the configured Mini DE fields.

• Search for specific AI-generated values
  Quickly identify articles where AI has extracted a particular value or data.

• Combine with existing filters
  Mini DE AI filters can be used alongside other article and screening filters to narrow down the required set of articles.

• Support faster review and validation
  Quickly identify articles that require validation or further review based on AI-extracted Mini DE values.

• Improve visibility into AI extraction results
  Project owners and reviewers can more easily analyse the information generated by AI across the project.

Benefits

Faster Navigation
Quickly filter articles based on AI-extracted Mini DE values.

Better Data Validation
Identify and review AI-extracted information more efficiently.

Improved Workflow Efficiency
Reduce the need to manually search through large datasets.

Better Use of AI-Generated Data
Turn Mini DE outputs into actionable filters that can support screening, analysis, and downstream workflows.

We’ve introduced a new enhancement that allows teams to carry the final Mini Data Extraction (Mini DE) values from the Title–Abstract stage into the Full-Text screening stage.

This helps reviewers avoid re-entering information that has already been captured during Title–Abstract screening and ensures that the extracted data remains available during screening.

What’s New?

The final Mini DE values at Title–Abstract screening can now automatically be copied to the Full-Text screening.

This is particularly useful when all or some fields will be same at both screening stages.

Key Benefits

• Avoid duplicate data entry: Reviewers do not need to manually re-enter information already captured during Title–Abstract screening.

• Carry forward existing data: Final Mini DE values remain available when the article moves to Full-Text screening.

• Maintain continuity: Teams can continue working with the information collected during the Title–Abstract screening stage.

• Save reviewer time: Reduces repetitive data entry, particularly across large review projects.

• Support downstream extraction: Early study characteristics can be carried forward and used as a starting point during Full-Text review.

How It Works

When the Mini DE form is copied from Title–Abstract to Full-Text, the final values recorded for the Mini DE fields are carried forward to the Full-Text stage.

Reviewers can then review the existing values, and continue with the Full-Text screening workflow. Additional fields can also be added at the Full-Text stage when required. AI can also be run 

This enhancement provides a more connected Mini Data Extraction workflow across screening stages and helps teams build on information already collected rather than repeating the same work.

We’ve enhanced the Conflicts section in EasySLR by introducing two dedicated subtabs to help reviewers quickly identify which conflicts require their attention and which conflicts involve their own decisions.

This update provides a clearer separation of responsibilities and makes it easier to manage conflicts without confusion around who can take the next action.

What’s New?

The Conflicts section now includes two subtabs:

• Conflicts You Can Resolve

• My Decision Conflicts

These subtabs help users quickly distinguish between conflicts they can resolve and conflicts involving articles where they are one of the primary reviewers.

1. Conflicts You Can Resolve

The Conflicts You Can Resolve subtab displays conflicts where the logged-in user is authorised to take the final resolution decision. 

Users can access the relevant articles, review the decisions submitted by other reviewers, and resolve the conflict. 

Key benefits

• Quickly identify conflicts requiring your action

• Focus only on conflicts you are authorised to resolve

• Review conflicting decisions before making the final decision

2. My Decision Conflicts

The My Decision Conflicts subtab displays conflicts involving decisions submitted by the logged-in user.

This section helps reviewers see and follow up on conflicts involving their decisions. However, they cannot resolve the conflict themselves.

If needed, reviewers can update their own decision after discussing it internally and reaching an agreement.

This allows reviewers to correct or revise their decisions without giving them access to resolve the conflict independently.

How the Two Subtabs Work Together

The new structure makes it easier to understand the user's role in each conflict:

Clearer Conflict Management

Previously, users could see only the  conflicts between conflicts they could resolve and not the conflicts where they were one of the reviewers involved.

With this enhancement, EasySLR provides a more structured view of conflict-related tasks, helping users:

• Understand their responsibility for each conflict

• Quickly identify conflicts requiring action

• Avoid attempting to resolve conflicts they are not authorised to resolve

• Update their own decisions when necessary

• Maintain a clear and transparent conflict resolution workflow

This enhancement makes conflict management more organised while ensuring that conflict resolution remains with the designated reviewers.

We’ve upgraded the AI models used for Full-Text (FT) Screening and Data Extraction (DE) in EasySLR to GPT-5.6 Luna.

This upgrade is designed to provide stronger AI-assisted processing across complex research content while improving the overall quality and consistency of screening and extraction workflows.

What’s Improved?

With the new model, users can benefit from:

• Improved understanding of complex research content and scientific terminology

• Better contextual reasoning when evaluating information across full-text publications

• More consistent interpretation of study characteristics and extraction requirements

• Enhanced handling of complex data extraction tasks, including information presented across different sections of a publication

• Improved support for AI-assisted screening and extraction workflows

The upgraded model is now being used for Full-Text Screening and Data Extraction in EasySLR.

Track AI Models Used in EasySLR

EasySLR uses different AI models across various features and workflows. To provide greater transparency, we maintain a dedicated help article where you can view the latest list of AI models used across the platform, along with the features where they are applied.

View the complete AI Model List: https://support.easyslr.com/portal/en/kb/articles/list-of-ai-models-used-across-easyslr

We will continue to evaluate and update the underlying AI models as newer and more capable models become available, while maintaining transparency around the models used across EasySLR.

We’ve enhanced the AI Agent workflow by adding automated de-duplication as a. This allows duplicate records to be identified and marked automatically as part of the review workflow, reducing the need for manual intervention.

What’s New?

De-duplication can now run automatically within the AI Agent workflow.

Once configured, the AI Agent can automatically process imported records for potential duplicates and mark them before moving them to the subsequent stages of the review workflow.

Key Benefits

• Automated duplicate detection: Identify and mark potential duplicate records without running a separate manual process.

• Reduced manual effort: Minimise the time spent manually initiating and managing de-duplication.

• Streamlined workflow: Combine de-duplication with other AI Agent steps for a more automated review process.

How It Works

1. Configure the AI Agent workflow for your project.

2. Setup De-duplication criteria (Default is DOI and Author).

3. Once the workflow runs, EasySLR automatically processes the records through the de-duplication step.

4. Potential duplicate records are identified and marked based on the configured de-duplication criteria.

5. The workflow then proceeds with the subsequent configured steps.

Why It Matters

De-duplication is an important early step in systematic reviews, particularly when records are imported from multiple databases. Automating this step helps teams reduce repetitive manual work and move more efficiently from literature import to screening.

Overview

The Plot Digitizer in EasySLR enables researchers to extract numerical data from graphs and charts contained within scientific publications. Instead of manually estimating values from visual plots, the tool converts graphical information into structured, reusable data that can be reviewed, copied, and incorporated into your evidence synthesis workflow.

This feature is particularly useful when numerical values are presented only as figures and are not available in tables or the main text of the publication.

Why Use the Plot Digitizer?

Many research articles present important outcomes only as graphs, making manual data extraction both time-consuming and prone to error.

The Plot Digitizer helps researchers:

• Capture data from published figures more efficiently.

• Reduce manual transcription effort.

• Improve consistency during data extraction.

• Validate graphical results more easily.

• Reuse extracted values for downstream analysis.

Supported Plot Types

The Plot Digitizer supports a variety of commonly used scientific figures, including:

• Line graphs

• Scatter plots

• Bar charts

• Column charts

• Time-series plots

• Multi-series graphs

• KM-Curve

How to Access the Plot Digitizer

1. Navigate to easyslr.com.

2. Click Tools.

3. Select the Plot Digitizer option.

4. Upload the image of the figure.

5. Mark the Axis.

6. Add Datasets.

7. Click Run Extract Curve

How the Plot Digitizer Works

The tool analyses the selected figure, extracts the underlying numerical data, and presents the results in a structured table for easy review. Users can verify the extracted values before using them in their analysis.

Reviewing the Extracted Data

Once processing is complete, EasySLR displays the extracted values in an easy-to-read tabular format.

Users can:

• Review individual data points.

• Verify extracted values against the original figure.

• Compare multiple data series.

• Confirm the accuracy before using the results.

Export Extracted Values

After reviewing the output, users can quickly export the extracted data.

Typical use cases include:

• Data extraction forms

• Excel worksheets

• Statistical software

• Meta-analysis preparation

• Internal reports

Best Practices

For the best results:

• Use clear, high-resolution PDF figures whenever possible.

• Verify extracted values against the original graph before finalising data extraction.

• Review multi-series graphs carefully to ensure the correct series has been identified.

• Where appropriate, cross-reference extracted values with any supporting tables or text within the publication.

Common Use Cases

Researchers commonly use the Plot Digitizer to extract:

• Survival curves

• Clinical outcome measurements

• Biomarker trends

• Response rates over time

• Treatment comparisons

• Safety outcome graphs

• Longitudinal study results

Frequently Asked Questions

Does the Plot Digitizer modify the original PDF?

No. The original publication remains unchanged. The extracted data can be separately exported in multiple formats.

Can I use the extracted values in my data extraction form?

Yes. The extracted values can be exported and incorporated into your project workflow after verification.

Should I review the extracted data?

Yes. While the Plot Digitizer greatly reduces manual effort, it is recommended that researchers validate the extracted values against the original figure before using them in analyses or reports.

Summary

The Plot Digitizer helps researchers convert graphical information from scientific publications into structured numerical data in just a few clicks. By reducing manual effort and improving the accessibility of figure-based results, it streamlines evidence extraction and supports faster, more efficient literature reviews while maintaining researcher oversight.

We've enhanced the Background Tasks experience in EasySLR by making the failure reasons more visible, easier to understand, and simpler to resolve.

Previously, if a background task failed, users had limited visibility into what went wrong. With this update, failed tasks now provide detailed information, allowing users to quickly identify the affected articles, understand the reason for the failure, and take corrective action.

What's New?

Background Tasks now provide enhanced reporting whenever a task cannot be completed successfully.

In addition to displaying the task status, EasySLR now allows users to:

• View detailed messages for failed tasks.

• Identify exactly which articles were affected.

• Download a complete error report in Excel for easier troubleshooting and bulk corrections.

Article-Level Error Visibility

When a background task encounters an issue, EasySLR now identifies the individual articles that could not be processed successfully.

This makes it much easier to isolate problematic records instead of having to review the entire dataset.

Download Report

Users can now download an Excel report containing all failed articles along with the corresponding reason for failure.

The report can be used to:

• Review failed records offline.

• Identify patterns across failures.

• Make the required corrections.

• Re-upload or rerun processing only for the affected articles.

This significantly reduces the time required to diagnose and resolve processing issues.

Clearer Messages

Background Tasks now display more descriptive messages, making it easier to understand the underlying cause of a failure.

Examples include issues related to:

• File validation

• AI processing

• PDF retrieval

• Import or export operations

• Configuration settings

• System or connectivity issues

Faster Troubleshooting

Improved visibility allows users to resolve issues without repeatedly rerunning the same task.

By knowing exactly which articles failed and why, users can make targeted corrections before initiating another run.

Summary

The enhanced Background Tasks experience provides greater transparency by identifying failed articles, displaying detailed error messages, and allowing users to download an Excel report containing the affected records and reasons for failure. These improvements make troubleshooting significantly faster, reduce manual effort, and provide a smoother experience when working with AI processing, imports, exports, and other background operations in EasySLR.

We've added a new AI-powered Data Extraction Form Import tool in EasySLR, making it significantly easier to migrate your existing data extraction templates into the platform.

Instead of manually recreating extraction fields, users can now upload their existing Excel-based data extraction forms, and EasySLR's AI will automatically convert them into a platform-compatible template.

This enhancement drastically reduces project setup time while ensuring teams can continue using familiar extraction forms and workflows.

What's New?

EasySLR now allows you to import your existing data extraction Excel forms directly into the platform.

Using AI, the uploaded workbook is automatically analysed and converted into an EasySLR-compatible data extraction template.

This eliminates the need to manually recreate extraction sections, fields, dropdowns, and other components within the platform.

How It Works

Step 1: Import Your Existing Form

Navigate to the Data Extraction Settings and select Import from Excel.

Upload your existing data extraction workbook that you currently use for your systematic reviews.

Step 2: Provide Additional Instructions (Optional)

If your extraction form contains unique formatting or project-specific requirements, you can provide additional notes or instructions to guide the AI during the conversion process.

Examples include:

• Preferred grouping of fields

• Custom section names

• Instructions for handling merged cells

• Special formatting requirements

• Any project-specific extraction logic

These optional instructions help improve the quality of the generated template.

Step 3: AI Converts the Workbook

EasySLR's AI analyses the uploaded workbook and automatically generates a platform-compatible data extraction template.

The AI intelligently interprets:

• Sections

• Field names

• Dropdown options

• Repeating data structures

• Overall workbook layout

The converted template is then prepared for review before being applied to the project.

Step 4: Review the Generated Template

Once the conversion is complete, users can download the generated workbook to verify that all fields have been mapped correctly.

This review step allows users to ensure the generated template accurately reflects their original extraction form before using it in a live project.

Step 5: Refine if Needed

If any adjustments are required, users can simply rerun the conversion.

Additional notes or instructions can be provided to further guide the AI and improve the output.

This iterative workflow allows users to progressively refine the generated template until it matches their requirements.

Step 6: Apply to Project

Once satisfied with the converted template, simply click Apply to Project.

The data extraction form is then added to the project and is immediately available for reviewers.

Why This Matters

Many research teams already have established data extraction forms developed over several years.

Recreating these templates manually inside a new platform can be both time-consuming and error-prone.

This new AI-powered import feature enables teams to transition to EasySLR much faster while preserving their existing extraction methodology.

Key Benefits

Significantly Faster Project Setup: Eliminate the need to manually recreate large data extraction templates.

Preserve Existing Workflows: Continue using your existing extraction forms while taking advantage of EasySLR's collaborative and AI-powered workflow.

AI-Assisted Template Generation: Automatically converts Excel-based extraction forms into platform-ready templates with minimal manual effort.

Flexible Iterative Refinement: If required, rerun the conversion with additional instructions until the generated template fully meets your expectations.

Reduced Manual Errors: Minimise mistakes that can occur when manually recreating complex extraction forms.

Summary

The new tool simplifies one of the most time-consuming parts of systematic review setup.

By allowing users to upload their existing Excel-based extraction forms and automatically converting them into EasySLR-compatible templates, the platform drastically reduces setup effort while preserving established workflows.

Combined with the ability to review, refine, and reprocess templates before applying them to a project, this enhancement provides a faster, more accurate, and significantly more flexible way to configure data extraction in EasySLR.

We've extended the AI Include Strictness setting to Full-Text AI Screening also, giving researchers greater control over how conservatively or liberally the AI makes inclusion decisions during full-text review.

Previously available for Title & Abstract Screening, this capability is now also applied during the Full-Text Screening stage, allowing teams to fine-tune AI behaviour based on the objectives and rigor of their review.

What's New?

The AI Include Strictness setting is now extended to Full-Text AI Screening.

Researchers can choose how strict the AI should be when evaluating full-text articles against the review protocol.

This allows teams to balance:

• Maximising sensitivity by identifying as many potentially relevant studies as possible.

• Increasing specificity by reducing false-positive inclusions.

How It Works

The strictness setting adjusts the AI's decision-making threshold when determining whether a study should be included.

Users can configure the AI to be:

Conservative (Higher Strictness)

The AI applies stricter inclusion criteria and recommends inclusion only when the article strongly satisfies the protocol requirements.

This setting is useful when:

• Inclusion criteria are highly specific.

• Teams want to minimise false-positive inclusions.

• Reviews require a high level of precision.

Balanced (Medium Strictness)

The AI applies a balanced decision-making approach, providing a good trade-off between Recall and Precision.

This setting is suitable for most systematic reviews and evidence synthesis projects.

Liberal (Lower Strictness)

The AI is more inclusive and recommends inclusion whenever sufficient supporting evidence is identified.

This helps maximise Recall by ensuring potentially relevant studies are less likely to be missed.

This setting is particularly useful for:

• Early-stage evidence reviews.

• Targeted Literature Reviews (TLRs).

• Broad scoping reviews.

• Reviews where missing relevant evidence carries a greater risk than reviewing additional articles.

Why This Matters

Different review projects require different screening strategies.

By extending AI Include Strictness to Full-Text Screening, researchers can now configure AI behaviour independently for both screening stages.

For example:

• Use a more liberal approach during Title & Abstract Screening to maximise Recall.

• Apply a stricter approach during Full-Text Screening to improve Precision before final study selection.

This provides greater flexibility while maintaining a transparent and reproducible review process.

We've enhanced the Statistics section in EasySLR by introducing Precision and F1 Score metrics for both human reviewers and AI-assisted screening. These additions provide deeper insights into screening performance and help teams evaluate the quality and consistency of review decisions across their projects.

By making these metrics available for both human and AI reviewers, EasySLR enables teams to compare performance, monitor screening quality, and better understand the effectiveness of their review workflows.

What's New?

The Quality dashboard in Statistics section now includes two additional AI performance metrics:

• Precision

• F1 Score

These metrics are calculated for both:

• Human reviewers

• AI reviewers

They are available alongside the existing screening statistics, allowing project owners to evaluate reviewer performance using widely accepted machine learning and information retrieval measures.

Precision

Precision measures how many of the articles marked as included by a reviewer (human or AI) were ultimately considered relevant when compared with the final review decisions.

A higher Precision score indicates fewer false-positive inclusion decisions, resulting in more accurate screening outcomes.

This metric helps teams understand how confidently both human reviewers and AI identify relevant studies during the screening process.

F1 Score

The F1 Score provides a balanced measure of screening performance by combining both Precision and Recall into a single metric.

Rather than evaluating only one aspect of reviewer performance, the F1 Score assesses how effectively both human reviewers and AI balance:

• Identifying relevant studies (Recall)

• Avoiding incorrect inclusions (Precision)

This provides a more comprehensive view of overall screening quality.

Why This Matters

Evaluating both human and AI performance using standardised metrics provides greater transparency into the screening process.

While Recall measures how effectively relevant studies are identified, Precision measures the accuracy of inclusion decisions. The F1 Score combines both measures into a single indicator, enabling teams to objectively assess screening performance regardless of whether decisions are made by human reviewers or AI.

This is particularly valuable for teams adopting AI-assisted workflows, comparing reviewer performance, or monitoring consistency across multiple reviewers.

Key Benefits

Comprehensive Performance Evaluation

Evaluate the screening quality of both human reviewers and AI using recognised performance metrics.

Better Reviewer & AI Validation

Understand how closely reviewer decisions align with final outcomes and identify opportunities to improve screening consistency.

Improved Quality Monitoring

Compare reviewer performance across projects, monitor AI effectiveness, and identify areas requiring additional training or workflow refinement.

Increased Transparency

Gain clearer visibility into the accuracy and effectiveness of both human and AI screening decisions, supporting more informed quality assurance and adoption of AI-assisted workflows.

Common Use Cases

Researchers and project owners can now:

• Evaluate the screening quality of both human reviewers and AI

• Compare reviewer performance across projects

• Monitor AI performance alongside human reviewers

• Assess improvements after refining screening protocols or reviewer training

• Validate AI-assisted workflows before broader adoption

• Report standardised reviewer and AI performance using internationally accepted evaluation metrics

Summary

The addition of Precision and F1 Score to the Statistics dashboard provides a more complete and transparent view of screening performance for both human reviewers and AI.

By evaluating reviewer accuracy and consistency using standardised metrics, EasySLR helps research teams monitor screening quality, improve workflow efficiency, and make more informed decisions when conducting AI-assisted systematic reviews.

We've improved the AI Rerun workflow by introducing a new option to Run AI on Articles in Conflict. This addition gives reviewers another targeted way to rerun AI on records that require further assessment, helping teams resolve reviewer disagreements more efficiently.

Previously, AI could be rerun using several predefined selection criteria. With this update, Articles in Conflict has been added as an additional reason, allowing AI to specifically focus on records which are in conflicts.

What's New?

New AI Rerun Option: Articles in Conflict

A new option has been added to the existing Run AI options.

Users can now choose to rerun AI specifically on articles that are currently In Conflict.

This enables AI to provide fresh suggestions and supporting rationale based on the latest changes made to the Protocol.

Why This Matters

Conflicts are a natural part of systematic review workflows and often require additional evaluation before a final decision can be made.

Previously, there was no way to rerun AI on a selected set of articles if any amendments are made to Protocol to align AI and human reviewer decisions.

With this update when Run AI on Articles in Conflict is selected, EasySLR automatically processes only those records which are currently in conflict.

Complements Existing AI Selection Options

The new Articles in Conflict option has been added alongside the existing options available for bulk AI rerun.

This provides greater flexibility by allowing users to target AI processing based on specific workflow needs rather than rerunning AI across a broader set of records.

Key Benefits

Faster Conflict Resolution with AI: Quickly rerun AI on articles in conflict, with support for multiple AI iterations to help reduce conflicts and reach consensus more efficiently.

Reduced Manual Effort: Eliminate the need to manually identify and select articles before rerunning AI.

More Efficient AI Usage: Focus AI processing on articles where it is most valuable, improving review efficiency while avoiding unnecessary AI execution on already resolved records.

Summary

The new Run AI on Articles in Conflict enhancement makes conflict resolution more targeted and efficient.

By adding Articles in Conflict as an additional bulk AI rerun option, EasySLR enables review teams to quickly obtain AI recommendations for unresolved conflicts, helping accelerate consensus decisions while improving the overall efficiency of the evidence synthesis workflow.

We've introduced a new enhancement to the Duplicate Screening workflow by displaying the Source associated with each record. This provides reviewers with greater visibility into where each citation originated, making duplicate resolution faster, more transparent, and better informed.

The source information is automatically mapped from the Source selected during citation file upload and is carried forward throughout the duplicate screening process.

What's New?

Source Information for Duplicate Records

Previously, duplicate records displayed citation details but did not indicate the database or source from which they were imported.

With this update, every duplicate record now displays its associated Source, allowing reviewers to quickly identify the origin of each citation during duplicate comparison.

The source is automatically populated based on the source selected when the citation file was uploaded into EasySLR.

Examples include:

• PubMed

• Embase

• Scopus

• Web of Science

• ClinicalTrials.gov

• OpenAlex

• Other imported sources

Automatically Mapped During Import

When importing citations, users can specify the source of each file.

EasySLR now automatically preserves this information and displays it during Duplicate Screening without requiring any additional configuration.

This ensures citation provenance is maintained throughout the review workflow.

Why This Matters

Many systematic reviews involve importing citations from multiple literature databases.

During duplicate resolution, reviewers often need to understand where duplicate records originated before deciding which version to retain.

Displaying the upload source provides this context directly within the screening interface, eliminating the need to manually cross-reference imported files.

Better Duplicate Resolution

Source visibility enables reviewers to make more informed duplicate decisions by identifying:

• Which databases contain the duplicate record

• Whether the citation originates from a preferred or authoritative database

• Duplicate records imported from multiple search sources

This additional context simplifies duplicate comparison and improves decision consistency.

Key Benefits

Improved Duplicate Review: Quickly identify the source database for every duplicate record during comparison.

Better Decision-Making: Retain records from preferred or authoritative databases while resolving duplicates.

Enhanced Traceability: Maintain complete visibility into citation provenance throughout the review process.

Reduced Manual Verification: Eliminate the need to revisit original imports to determine where a citation originated.

Summary

This enhancement provides reviewers with immediate access to citation source information during duplicate resolution.

By automatically carrying forward the upload source selected during citation import, EasySLR makes duplicate screening more transparent, efficient, and methodologically robust while preserving the provenance of imported literature throughout the evidence synthesis workflow.

We've introduced a major enhancement to Workflow and AI Copilot settings in EasySLR, providing significantly greater flexibility in how screening workflows are configured and managed.

Users can now independently configure reviewer requirements and AI behavior at each stage of the review process, enabling workflows that more closely align with project methodologies and team preferences.

This enhancement makes it easier to design review processes that balance reviewer effort, AI involvement, quality control, and efficiency.

What's New?

Independent Reviewer Requirements by Screening Stage

Previously, reviewer requirements were managed through a single setting across the screening workflow.

With this update, reviewer requirements can now be configured separately for:

• Title & Abstract Screening

• Full-Text Screening

This allows teams to define different review requirements at each stage based on project complexity and methodology.

Examples include:

• Single review at Title & Abstract Screening and dual review at Full-Text Screening

• Dual review at Title & Abstract Screening and single review at Full-Text Screening

• AI-assisted workflows with different reviewer requirements at each stage

Why This Matters

Different stages of the review process often require different levels of scrutiny.

For example:

• Title & Abstract Screening may involve a larger volume of records where only one vote may be required.

• Full-Text Screening may require additional reviewer oversight.

The new configuration provides the flexibility to tailor reviewer requirements to each stage rather than applying a single rule across the entire workflow.

Enhanced AI Copilot Configuration

AI controls have also been redesigned to support independent stage-wise configuration.

Users can now configure AI separately for:

• Title & Abstract Screening

• Full-Text Screening

• Data Extraction

• Mini Data Extraction

This provides much greater flexibility in determining where and how AI participates throughout the review process.

Configure AI Differently at Each Stage

Previously, AI configuration was more tightly coupled across the workflow.

Now users can independently choose how AI behaves at each stage.

Examples include:

• AI Assistant during Title & Abstract Screening

• AI Reviewer during Full-Text Screening

This enables teams to adopt AI only where it delivers the most value.

Multiple AI Participation Modes

For each stage, users can now independently choose between:

AI Assistant

AI provides:

• Suggestions

• AI Notes

• PICOS extraction

• Supporting rationale

while human reviewers remain responsible for final decisions.

AI as One of the Reviewers

AI acts as an independent reviewer alongside human reviewers.

This is particularly useful for:

• Dual-review workflows

• Quality control processes

• Reviewer agreement assessments

AI as the Only Reviewer (Where Applicable)

AI can also be configured to independently review records without requiring human reviewer decisions at that stage.

This can significantly accelerate screening workflows where automation is preferred.

Key Benefits

Greater Workflow Flexibility: Configure different reviewer requirements and AI behaviour at each stage of the review process.

Better Alignment with Review Methodology

Support a wider range of workflows, including:

• Single-review workflows

• Dual-review workflows

• AI-assisted reviews

• AI reviewer workflows

• Hybrid review models

Enhanced Quality Control

Configure AI Reviewer functionality at selected stages to provide additional review oversight and consistency.

Increased Efficiency

Reduce reviewer workload by tailoring both reviewer requirements and AI involvement according to project needs.

Example Workflow Configurations

Example 1: AI-Assisted Screening

Title & Abstract Screening

• 1 Reviewer

• AI Assistant Enabled

Full-Text Screening

• 2 Reviewers

• AI Assistant Enabled

Example 2: AI Reviewer Workflow

Title & Abstract Screening

• 2 Reviews Required

• AI Reviewer Enabled

Full-Text Screening

• 2 Reviews Required

• Human Reviewers Only

• AI Assistant Enabled

Example 3: Hybrid Workflow

Title & Abstract Screening

• AI Assistant

Full-Text Screening

• AI Reviewer

This flexibility allows teams to build workflows that best fit their review objectives and resource availability.

Summary

The new Stage-Wise Reviewer & AI Configuration enhancement provides complete control over how reviews are conducted throughout the EasySLR workflow.

By allowing reviewer requirements and AI behaviour to be configured independently at each stage, teams can build more efficient, flexible, and methodologically appropriate review workflows while maximising the benefits of AI-assisted evidence synthesis.

We've enhanced EasySLR's PDF fetching capabilities by adding PubMed Central (PMC) Open Access as an additional trusted source for automatic full-text retrieval.

This improvement increases the number of articles for which EasySLR can automatically locate and attach PDFs, reducing the need for manual searching and uploads.

This enhancement is particularly beneficial for medical, healthcare, life sciences, and biology-related reviews, where a large number of publications are available through PubMed Central.

What's New?

EasySLR can now automatically search PubMed Central Open Access when retrieving full-text PDFs.

If an article has any of the following identifiers available:

• DOI

• PMID

• PMCID

• PubMed URL

• PMC URL

EasySLR will automatically attempt to locate an open-access PDF available through PubMed Central and attach it to the article.

If Auto PDF Fetching is enabled, the entire process happens automatically in the background.

How Does It Work?

EasySLR now uses PubMed Central's infrastructure to intelligently retrieve article identifiers and identify available open-access PDFs.

The platform will:

1. Use available identifiers (DOI, PMID, PMCID, PubMed URL, or PMC URL)

2. Search the PubMed Central Open Access dataset

3. Detect valid PDF locations

4. Automatically fetch and attach the PDF to the corresponding citation

This significantly expands EasySLR's full-text retrieval coverage.

Why This Matters

Finding and attaching full-text PDFs is often one of the most time-consuming steps in evidence synthesis projects.

Review teams frequently spend substantial time:

• Searching for missing PDFs

• Downloading files manually

• Matching PDFs to citations

• Managing incomplete full-text libraries

By expanding EasySLR's retrieval sources, more articles can now be populated automatically, allowing reviewers to focus on screening and evidence synthesis rather than administrative tasks.

Key Benefits

Improved PDF Coverage

EasySLR can now retrieve additional PDFs that may not have been available through existing sources.

This increases the likelihood of obtaining full texts automatically.

Reduced Manual Effort

Spend less time searching for and uploading PDFs manually.

More citations will arrive with their full texts already attached.

Better Support for Healthcare & Life Sciences Reviews

This enhancement is especially valuable for:

• Systematic Reviews

• Rapid Reviews

• Targeted Literature Reviews (TLRs)

• Scoping Reviews

• Evidence Mapping projects

within:

• Healthcare

• Life Sciences

• Medicine

• Biology

• Clinical Research

where PubMed Central hosts a significant number of open-access publications.

Faster Progression to Full-Text Screening

With more PDFs automatically available, teams can move into Full-Text screening sooner without waiting to complete manual PDF collection.

End Result

More PDFs retrieved automatically. Less manual searching. Faster evidence synthesis workflows.

This enhancement is another step towards reducing manual overhead and allowing review teams to spend more time analysing evidence rather than managing files.

We've completely revamped the Mini Data Extraction form setup experience in EasySLR to create a cleaner, more intuitive, and more efficient workflow for reviewers.

This enhancement focuses on improving usability by introducing a better structured interface, clearer call-to-actions (CTAs), and a more organised way of configuring extraction fields.

What's New?

The Mini Data Extraction setup page has been redesigned to make form creation faster, easier to navigate, and simpler to manage, especially for larger extraction forms.

The update includes:

• Cleaner and more organised UI

• Improved field grouping and data structuring

• Clearer action buttons and workflows

• New field reordering functionality

• New option to copy Title & Abstract forms to Full-Text (and vice versa)

• Reduced visual clutter

Key Enhancements

Cleaner & More Intuitive Interface

The overall layout has been redesigned to improve readability and reduce complexity.

Users can now focus on configuring extraction fields without navigating through a crowded interface.

Benefits include:

• Improved visibility of extraction fields

• Better spacing and alignment

• Reduced scrolling

• Faster navigation

Improved Data Structuring 

Extraction fields are now organised in a more structured manner.

This makes it easier to:

• Understand the overall extraction setup

• Group related fields together

• Review extraction configurations before running AI

The improved structure is particularly beneficial for larger and more complex extraction forms.

Clearer Call-to-Actions (CTAs)

Action buttons in the setup process have been redesigned to make the workflow more intuitive.

Users can now more easily identify actions such as:

• Adding new fields

• Editing existing fields

• Reordering fields

• Saving changes

• Delete Fields

This reduces confusion and makes form setup faster for both new and experienced users.

New: Reorder Extraction Fields

Users can now easily reorder Mini Data Extraction fields.

This allows teams to arrange fields in a logical sequence that aligns with their extraction workflow.

Benefits include:

• Better organisation of extracted outputs

• More standardised extraction templates

• Improved readability for reviewers

• Greater flexibility when modifying existing forms

New: Copy Title & Abstract Form to Full-Text (and Vice Versa)

Users can now directly copy extraction forms between the Title & Abstract and Full-Text stages.

This eliminates the need to manually recreate similar extraction fields across both stages.

Benefits include:

• Significant time savings during setup

• Reduced duplication of effort

• Improved consistency between stages

• Easier maintenance of extraction forms

This is particularly useful for projects where similar information needs to be captured at both screening stages.

Reduced Visual Clutter

The interface has been streamlined to minimise distractions and improve focus during setup.

This creates a smoother user experience, particularly for projects with a large number of extraction fields.

Key Benefits

Faster Form Setup: Spend less time configuring extraction forms and more time reviewing evidence.

Better User Experience: A cleaner interface makes the setup process more intuitive and easier to learn.

Improved Organisation: Create more structured extraction forms that are easier to review and maintain.

Easier Navigation: Locate actions and manage extraction fields more efficiently.

Reduced Duplicate Work: Copy forms across stages instead of recreating them manually.

Better Scalability: The redesigned interface is particularly helpful for projects with larger and more complex extraction requirements.

This revamp is aimed at making Mini Data Extraction setup faster, cleaner, and easier to use, helping teams build and manage extraction templates with greater efficiency.

We've added new Supplementary Hints within the EasySLR PDF Viewer to help reviewers quickly identify whether a publication has associated supplementary materials available.

This enhancement improves document visibility, making it easier for reviewers to identify supplementary files early in the workflow rather than discovering them later during screening or data extraction. By clearly indicating the hints of supplementary, teams can access supporting information faster and reduce the risk of overlooking important study details during the review process.

What's New?

EasySLR will now display a visual hint within the PDF Viewer whenever a publication potentially has associated supplementary files. This allows reviewers to immediately identify the availability of additional supporting material alongside the main publication, eliminating the need to manually check each PDF for supplementary content and making the review process faster and more efficient.

Why This Matters

In many evidence synthesis projects, important information is also present in supplementary documents along with the main PDF.

Examples include:

• Detailed outcome data

• Extended baseline characteristics

• Additional subgroup analyses

• Statistical methods

• Study appendices

• Tables and figures not included in the main PDF

Without clear visibility, supplementary files can easily be overlooked during screening and data extraction. Reviewers may also spend additional time checking every PDF individually for supplementary materials, rather than quickly identifying and focusing only on publications where supplementary files are actually available.

Key Benefits

Increased Visibility of Supplementary Content: Reviewers have prior information that supplementary materials may exist for a publication instead of discovering them later in the workflow.

Reduced Risk of Missing Critical Information: Teams can ensure that important evidence contained outside the primary PDF is considered during the review process.

Improved Data Extraction Completeness: Supplementary documents often contain valuable information required for screening and extraction, including:

• Outcome measures

• Detailed study characteristics

• Additional endpoints

• Statistical analyses

The new hint helps reviewers know when additional sources should be reviewed.

Faster Reviewer Workflows: Rather than manually checking every publication for supplementary files, reviewers can quickly identify articles that have supplementary hints within the main PDF.

Better Transparency Across the Review Process: Project teams can more easily understand which articles may have supporting documentation available, leading to more comprehensive evidence capture.

Why This Enhancement Matters

A significant amount of critical study information sometimes resides outside the primary publication. The new Supplementary Hint acts as an additional layer of visibility, helping reviewers identify potentially relevant supporting documents early and reducing the likelihood of missing important evidence during the review process.

We've added 2 new AI Decision Availability Filters for both the Title & Abstract (TiAb) and Full-Text (FT) screening stages, making it easier for reviewers and project owners to quickly identify whether an article has AI-decision or not.

This enhancement provides greater visibility into AI processing and helps teams identify records without AI decision. These filters are available throughout the platform, including Title & Abstract Screening, Full-Text Screening, QC workflows, and the Articles section, ensuring easy access to AI processing status at every stage of the review.

What's New?

Users can now filter articles based on the availability of an AI decision.

Available options include:

AI Decision Available

Displays articles where the AI has successfully processed the record and generated a screening decision.

No AI Decision

Displays articles where an AI decision is not available.

This may occur when:

• The article has not yet been processed by AI

• AI processing is still pending

• The document exceeds supported AI processing limits

Key Benefits

Quickly Monitor AI Processing Progress: Project owners can easily track which articles have already been reviewed by AI and which are not.

Identify Records Requiring Manual Review: The filter makes it simple to locate articles without AI decisions so reviewers can prioritise manual assessment where necessary.

Better Workflow Management

Teams can quickly separate:

• AI-processed records

• Pending records

This helps improve review planning and task allocation.

Faster Quality Control

Project owners can focus specifically on AI-reviewed articles when validating AI performance or conducting quality checks.

Improved Transparency

Provides clear visibility into AI coverage across the project, helping teams understand exactly which records have AI decisions.

Common Use Cases

• Identify articles that have AI decisions

• Locate records still without AI decisions

• Review AI coverage before progressing to the next stage

• Prioritise manual review of non-AI-processed articles

• Monitor AI workflow progress across large projects

Why This Matters

As projects scale, it becomes increasingly important to understand which records have been processed by AI and which still require reviewer attention.

The new AI Decision Filter provides a simple and effective way to monitor AI processing status, improve workflow visibility, and ensure that no articles are overlooked during the screening process.

We've introduced a new PDF Page Count Filter, giving teams greater visibility into document size and helping reviewers prioritise full-text screening activities more effectively.

This enhancement makes it easier to identify articles based on PDF length and quickly locate studies that may require additional reviewer attention. The filter is available throughout the platform, including Full-Text Screening, QC workflows, and the Articles section, ensuring easy access to the PDF page count at key stages of the review.

What's New?

Users can now filter articles using three PDF page count categories:

30 Pages or Less

Displays PDFs containing 30 pages or fewer.

These documents are eligible for AI processing and can receive AI-assisted full-text screening suggestions where applicable.

More Than 30 Pages

Displays PDFs containing more than 30 pages.

This filter helps reviewers quickly identify larger documents that require additional attention. Since AI currently does not generate full-text screening decisions for PDFs exceeding the supported page limit, these articles will require manual reviewer assessment.

This is particularly useful for identifying:

• Clinical Study Reports (CSRs)

• Health Technology Assessment (HTA) reports

• Long-form evidence reviews

• Publications with extensive supplementary content

Unidentified

Displays records where the PDF is empty, invalid, corrupted, or where page count information could not be determined.

This allows teams to quickly identify documents that may require:

• PDF replacement

• Re-uploading

• Additional quality checks before screening

Key Benefits

Better Visibility into AI Eligibility: Quickly identify which articles can be processed by AI and which will require manual reviewer decisions.

Faster Full-Text Review Planning: Understand document complexity and review effort before screening begins.

Easier Identification of Large Reports: Locate lengthy PDFs that may require additional reviewer time and resources.

Improved Quality Control: Quickly identify invalid, empty, or problematic PDF files through the Unidentified category.

Common Use Cases

• Identify PDFs eligible for AI-assisted screening (30 pages or less)

• Locate articles requiring manual review (more than 30 pages)

• Find missing, corrupted, or invalid PDFs (Unidentified)

• Better estimate full-text screening workload

• Improve project planning and reviewer allocation

We've enhanced the Quality Control (QC) workflow by introducing a new Screened By filter, making it easier for project owners to QC articles based on reviewer activity.

What's New?

Project owners can now filter articles in the QC tab based on the reviewers who screened them.

This allows teams to quickly focus on articles reviewed by specific individuals, making QC workflows more targeted and efficient.

New Filter Options

Filter by Individual Reviewer

Select a single reviewer to view all articles screened by that reviewer.

Use cases:

• Reviewing the decisions of a newly onboarded reviewer

• Performing targeted QC checks

• Monitoring reviewer-specific screening patterns

Filter by Multiple Reviewers

Select multiple reviewers simultaneously to narrow down the article list.

Project owners can then choose between two filtering modes:

Any Selected Reviewer

Displays articles that have been screened by at least one of the selected reviewers.

Useful when:

• Reviewing the combined work of a subgroup of reviewers

• Identifying all articles reviewed by specific team members

All Selected Reviewers

Displays only articles where all selected reviewers have taken decisions.

Useful when:

• Reviewing completed double-review records

• Performing QC on records that have been assessed by multiple reviewers

Key Benefits

Faster Quality Control Reviews: Quickly locate articles reviewed by specific team members without manually searching through records.

Improved Reviewer Oversight: Monitor reviewer-level screening activity and decision patterns more effectively.

Simplified Double-Review Management: Easily identify articles where multiple reviewers have completed their reviews.

Greater Flexibility for Large Teams: Particularly useful for projects involving multiple reviewers and large screening volumes.

We’ve expanded the options available within the Last Updated filter to make it easier for teams to identify articles reviewed within a selected time period and efficiently manage decision updates.

This enhancement improves visibility into recent review activity and helps users quickly track, revisit, and update screening decisions.

What’s New?

Additional filtering options have now been added to the Last Updated filter, allowing users to narrow down articles based on when they were recently reviewed or modified.

Users can now more easily:

• Identify recently reviewed articles

• Track screening activity over specific time ranges

• Locate articles requiring follow-up or decision updates

• Monitor reviewer activity more efficiently

Key Enhancements

Expanded Time-Based Filtering Options

The filter now provides more flexible date and time selection options, making it easier to retrieve articles updated within a specific duration.

Examples include:

• Last 1hr

• Last 3hr

• Last 6hr

• Last 24hr

• Custom date ranges

This allows teams to quickly focus on recent workflow activity without manually searching through large datasets.

Easier Decision Updates

The enhanced filter makes it significantly simpler to identify and revisit articles where decisions may need to be modified.

This is especially useful when:

• Inclusion/exclusion criteria are updated

• Protocol changes occur

• Reviewers need to revisit previously screened studies

Users can quickly filter relevant records and update decisions in bulk or individually.

We’ve redesigned the EasySLR sidebar to make better use of available screen space and create a cleaner, more efficient workflow experience.

The updated layout removes unnecessary empty spaces across the interface, allowing users to utilize more of the screen for actual review activities such as screening, PDF review, and data extraction.

What’s Improved?

Better Utilisation of Screen Space

The interface has been optimised to eliminate unused and unnecessary empty areas, ensuring that more content is visible on the screen at all times.

Key Enhancements

Adjustable Sidebar Size

Users can now resize the sidebar based on their preference and workflow needs.

This allows teams to:

• Expand workspace visibility while reviewing articles

• Customise the layout according to screen size and workflow stage

Cleaner & More Compact Navigation

Navigation sections have been reorganised to reduce clutter while keeping all major workflow stages easily accessible.

Users can quickly move between:

• Search & Import

• Screening

• Full-Text Review

• Data Extraction

• Quality Appraisal

• Statistics & Reports

• Project Settings

without large unused gaps or excessive spacing.

More Workspace for Core Review Activities

This is especially beneficial for teams working on laptops or smaller screens

Reduced Visual Clutter

The redesigned structure creates a cleaner interface with:

• Better spacing consistency

• Improved alignment

• More efficient content organisation

• Fewer distractions during long review sessions

Improved Experience Across Devices

The optimised layout improves usability across:

• Laptops

• Smaller monitors

• Split-screen workflows

• High-volume evidence synthesis projects

while making better use of every available part of the screen.

We’ve enhanced the Screening Excel export in EasySLR by adding two new columns:

• QC Status

• QC Done By

This update makes the screening download more comprehensive and allows teams to access key Quality Control (QC) information directly within the screening sheet itself.

What’s New?

QC Status Column

The Screening Excel file will now include the QC status for each article.

This helps users quickly identify:

• Whether QC has been completed

• Articles pending QC

• QC-reviewed records within the workflow

This improves visibility into the progress and completion status of Quality Control activities directly from the screening export.

QC Done By Column

A new QC Done By column has also been added to display the reviewer who performed the QC activity for each article.

This allows teams to:

• Track reviewer-level QC activity

• Identify who completed QC on specific records

• Improve auditability and reviewer accountability

• Simplify internal tracking and reporting workflows

Why This Enhancement Was Added

Previously, QC-related information was already available as part of the dedicated QC report.

However, teams often rely heavily on the Screening Excel sheet for:

• Reviewer tracking

• Decision analysis

• Workflow reporting

• Offline review and auditing

To make the Screening Excel export more complete and reduce dependency on multiple files, QC information has now been added directly into the screening sheet as well.

This ensures that users can access both screening and QC-related details in a single consolidated export.

Key Benefits

More Comprehensive Screening Export

Users now get both screening decisions and QC details together in one file.

Easier Reviewer Tracking

Teams can easily identify:

• Which reviewer completed QC

• Which decisions were overturned

• QC coverage across the project

Simplified Reporting & Auditing

The enhanced export makes it easier to:

• Conduct internal audits

• Review QC workflows

• Share consolidated reports with stakeholders

without needing to combine multiple reports manually.

Improved Workflow Transparency

The addition of QC metadata improves visibility into the review process and helps teams better understand reviewer-level activity.

Reduced Operational Overhead

Teams no longer need to separately refer to QC reports for basic QC tracking information, reducing manual reconciliation efforts.

This enhancement is aimed at making the Screening Excel download more informative, centralised, and aligned with real-world evidence synthesis and review management workflows.

We’ve introduced a new Auto Fetch PDF setting in EasySLR, giving teams greater flexibility in how PDFs are managed within projects.

What’s New?

Users can now choose whether they want EasySLR to automatically fetch PDFs for imported citations.

How It Works

• When Auto Fetch PDF is Enabled
  EasySLR will automatically begin fetching available PDFs for all articles in the project, helping teams quickly move into the full-text screening stage.

• When Auto Fetch PDF is Disabled
  EasySLR will not attempt to retrieve PDFs automatically, allowing teams to manage PDF uploads manually or use their preferred workflow.

How to Enable or Disable Auto Fetch PDF

Step 1: Open Project Settings

Step 2: Navigate to Workflow Section

Step 3: Locate the “Auto Fetch PDF” Setting

Step 4: Choose Your Preferred Workflow

Key Benefits

• Greater control over PDF management workflows

• Flexibility to decide when the PDFs should be fetched

• Reduced unnecessary PDF retrieval for projects where automatic retrieval is not required

• Faster access to full-text articles when enabled

This update allows teams to better align PDF retrieval behaviour with their internal review processes and project requirements.

EasySLR now supports Drag & Drop PDF uploads to make full-text management faster and more convenient. Previously, users had to browse and select PDFs manually for every upload. With this enhancement, users can now select multiple PDFs at once from their local system and simply drag and drop them directly into the platform.

What’s Improved

1. Faster Bulk Uploads

• Select multiple PDFs together and upload them in one action

• Eliminates the need to individually browse and select files repeatedly

• Makes handling large document sets much easier

2. Simplified Upload Workflow

• More intuitive and user-friendly upload experience

• Reduces the number of steps involved in PDF management

• Helps users move through Full-Text workflows more efficiently

3. Better Support for Large Reviews

• Particularly useful for projects with high article volumes

• Speeds up document uploads during Full-Text screening and Data Extraction

• Reduces manual effort in managing PDFs

Why This Matters

Managing hundreds of PDFs can be time-consuming when files need to be selected repeatedly. Drag & Drop uploads simplify this process by allowing users to upload files in bulk with minimal effort, improving workflow efficiency and saving time.

EasySLR now supports CustomID-based bulk upload of supplementary files, allowing teams to link additional materials (e.g., appendices, datasets, supplementary tables) to their corresponding articles using their own unique identifiers.

Overview

Managing supplementary files alongside primary PDFs can be complex—especially when teams rely on internally defined identifiers instead of RefIDs.

With this update, EasySLR enables bulk upload using CustomID matching, ensuring that supplementary files are automatically linked to the correct articles based on your project’s naming convention.

This enhancement provides greater flexibility while maintaining efficiency and accuracy across workflows.

What’s New

1. CustomID-Based File Matching

• Supplementary files can now be matched using CustomID naming conventions

• Automatically links files to the correct article during upload

• Supports projects using external IDs or internal tracking systems

👉 Example:
ABC123_appendix.pdf → Linked to article with CustomID ABC123

2. Bulk Upload Support

• Upload multiple supplementary files in a single batch

• Ideal for large-scale reviews with multiple files per study

• Minimises repetitive manual uploads

3. Support for Multiple Files per Article

• Attach multiple supplementary documents to a single study

• Useful for:

  • Appendices

  • Supplementary tables

  • Additional figures

  • Protocols or supporting documents

4. Improved File Organisation

• Supplementary files are automatically grouped under their respective articles

• Easily accessible during:

  • Full-text screening

  • Data extraction

• Enhances navigation and document management

5. Clear Upload Status & Visibility

• Easily identify:

  • Successfully matched files

  • Unmatched files

• Allows quick resolution before finalising uploads

6. Seamless Workflow Integration

• Works alongside existing CustomID-based PDF upload workflows

• Ensures consistency across all document types

• No changes required to existing project setup

How It Works

1. Prepare supplementary files in a local folder

2. Rename files using the CustomID format

   • Example: ABC123_supplementary_table.pdf

3. Navigate to:
   Uploads → Full-Text → Using ID → Upload → Select Supplementary Upload

4. Upload files in bulk

EasySLR will automatically:

• Match files using CustomID

• Link them to the corresponding articles

Why This Matters

This feature helps teams:

• Use their preferred or existing ID systems

• Avoid manual mapping of supplementary files

• Improve accuracy in document linking

• Keep all study-related materials organised in one place

• Scale workflows efficiently for large or complex projects

Important Notes

• CustomID in file names must exactly match the CustomID in the project

• Files without matching CustomIDs will be flagged as unmatched

• Manual upload remains available for unmatched files

EasySLR now enables users to download article sets directly from the Smart Tags visualization in Excel and RIS formats, making it easier to move from data exploration to actionable research outputs.

Overview

Smart Tags provide a visual representation of your dataset, helping you identify patterns, themes, and clusters across articles using AI-driven insights.

With this update, users can now export articles directly from these visualised segments, allowing you to quickly convert insights into structured datasets for further analysis—without needing to recreate filters manually.

What’s New

1. Export Directly from Visualized Data

• Download articles directly from selected tags or clusters

• Export reflects exactly what is being visualised

• Ensures alignment between analysis and output

2. Multiple Export Formats

Excel (.xlsx) for:

• Data analysis

• Reporting

• Collaboration

RIS format for:

• Reference managers (e.g., EndNote, Zotero)

• Further screening or research workflows

3. Improved Workflow Efficiency

• Move directly from visual exploration → dataset export

• No need to reapply filters in another section

• Reduces manual effort and duplication

4. Enable Targeted Research Analysis

• Export subsets for:

  • Thematic exploration

  • Subgroup or focused reviews

  • Sharing with collaborators or stakeholders

• Supports more structured and efficient research workflows

How It Works

1. Navigate to Smart Tags Visualization

2. Select a tag, cluster, or segment of interest

3. Open the list of associated articles

4. Click on Download

5. Choose format:

   • Excel or

   • RIS

6. Export the filtered dataset instantly

Why This Matters

This feature helps you:

• Translate visual insights into structured research datasets

• Export only the most relevant articles for analysis

• Improve efficiency in downstream workflows

• Maintain consistency between data exploration and reporting

Summary

With Smart Tags export, EasySLR allows you to:

• Analyse your data visually

• Select meaningful subsets of articles

• Download them instantly for further research and analysis

EasySLR now offers a built-in File Converter Tool, enabling users to quickly convert files into compatible formats required for seamless project setup and workflow execution—completely free of cost.

Access the tool here: https://www.easyslr.com/tools/file-converter

Overview

Handling multiple file formats across databases and tools can often slow down the review process. The File Converter Tool simplifies this by allowing users to convert files directly within the EasySLR ecosystem, eliminating the need for third-party tools.

This tool is completely free to use, making it easily accessible for all users without consuming any credits or requiring a subscription.

What’s New

1. Supported File Conversions

The tool currently supports seamless conversion between the following formats:

• RIS → NBIB, Excel

• NBIB → RIS, Excel

• Excel → RIS, NBIB

This ensures compatibility with commonly used databases and reference management tools.

2. Free to Use (No Credits Required)

• Available to all users 

• Does not consume AI credits

• Does not require an account on EasySLR 

3. No External Tools Required

• Eliminates dependency on third-party converters

• Keeps the entire workflow within EasySLR

• Reduces friction during project setup

4. Fast & Simple Conversion

• Upload → Select format → Download

• Designed for quick, one-step conversions

5. Improved Data Compatibility

• Ensures files are in the correct format for:

  • Citation import

  • Screening workflows

• Reduces formatting issues and upload errors

How It Works

1. Navigate to the File Converter Tool: https://www.easyslr.com/tools/file-converter

2. Upload your file (RIS, NBIB, or Excel)

3. Select the desired output format

4. Convert and download instantly

5. Use the converted file directly in your project

Summary

With the free File Converter Tool, EasySLR makes it easier to:

• Convert between RIS, NBIB, and Excel effortlessly

• Maintain compatibility across tools and databases

• Start projects faster with minimal setup friction

EasySLR now supports RefID-based bulk upload of supplementary files, enabling seamless linking of additional materials (e.g., appendices, datasets, supplementary tables) to their corresponding articles.

Overview

Managing supplementary files alongside primary PDFs can be time-consuming and error-prone when done manually. With this update, EasySLR allows you to bulk upload supplementary files using RefID matching, ensuring that each file is automatically linked to the correct article.

This enhancement improves efficiency, accuracy, and organisation during full-text review and data extraction stages.

What’s New

1. RefID-Based File Matching

• Supplementary files can now be matched using RefID naming convention

• Automatically links files to the correct citation during upload

• Eliminates the need for manual mapping

Example: 123_appendix.pdf → Linked to article with RefID 123

2. Bulk Upload Support

• Upload multiple supplementary files in a single batch

• Supports large-scale projects with multiple documents per study

• Reduces repetitive manual uploads

3. Support for Multiple Supplementary Files per Article

• Attach multiple supplementary documents to a single study

• Useful for:

  • Appendices

  • Supplementary tables

  • Additional figures

  • Protocol documents

4. Improved File Organisation

• Supplementary files are grouped under their respective articles

• Easy access during:

  • Full-text screening

  • Data extraction

• Enhances navigation and usability

5. Clear Upload Status & Visibility

• Identify:

  • Successfully matched files

  • Unmatched files

• Enables quick correction before final import

6. Compatibility with Existing PDF Workflow

• Works alongside existing RefID-based PDF upload system

• Maintains consistency across all document uploads

• No change required in existing workflows

How It Works

1. Prepare supplementary files in a local folder

2. Rename files using the RefID format

   • Example: 45_supplementary_table.pdf

3. Navigate to Uploads → Full-Text → Using ID→ Upload→ Select Supplementary Upload
   

   

4. Upload files in bulk

5. EasySLR automatically:

   • Matches files using RefID

   • Links them to the corresponding articles

Why This Matters

This enhancement helps teams:

• Save time by eliminating manual file mapping

• Reduce errors in linking supplementary materials

• Keep all study-related documents organised in one place

• Improve efficiency during screening and extraction

Important Notes

• RefID must match the article’s RefID exactly for successful linking

• Files without matching RefIDs will be flagged as unmatched

• Manual upload remains available for unmatched or exception cases

Summary

With RefID-based bulk upload for supplementary files, EasySLR makes it easier to:

• Manage multiple documents per study

• Maintain structured and organised project data

• Scale document handling for large reviews

EasySLR now supports an integrated Table and Graph Digitiser within the PDF viewer, enabling users to extract and utilise data directly from tables and figures.

Overview

Extracting data from tables and graphs in research papers is often time-consuming—especially when dealing with image-based content.

With this update, users can digitise tabular and graphical data directly within the PDF interface, review the extracted values, and add them to the data extraction form.

What’s New

1. In-PDF Table Extraction

• Detect and extract data from tables within PDFs

• Converts tables into editable, structured formats

2. Graph Digitisation

• Extract numerical values from graphs and charts

• Supports:

  • Line graphs

  • Bar charts

  • Scatter plots

• Enables capture of multiple data points for analysis

3. “View” Extracted Values

• After AI run, users can click on the PDF outline, click “View” to see all the values

• Displays structured data in an easy-to-review format

How It Works

1. Open the PDF in EasySLR

2. Click on the PDF outline

3. Select the Table or Graph 
   

   

4. Click “View” to access the data

   

5. Add relevant values directly to the data extraction window

Why This Matters

This enhancement helps teams:

• Extract complex data faster and more accurately

• Reduce manual effort and errors

• Seamlessly move from data capture to structured extraction

• Improve efficiency in meta-analysis workflows

Important Notes

• Manual validation is recommended for accuracy

• Results may vary based on PDF quality and resolution

• Users retain full control to edit and verify extracted data

Summary

With the Table & Graph Digitiser within the PDF viewer, EasySLR enables you to:

• Extract data directly from visual content

• Review values instantly using the “View” option

• Add data seamlessly into the extraction workflow

EasySLR now aligns with the latest RAISE 3 Guidelines, enhancing how users select, evaluate, and use AI tools responsibly within systematic review workflows.

Overview

The RAISE 3 framework focuses on responsible AI adoption in evidence synthesis, ensuring that AI usage is transparent, validated, and methodologically sound.

With this update, EasySLR integrates RAISE 3 principles directly into the workflow, complementing its existing methodology framework outlined here: https://www.easyslr.com/methods

This ensures that both methodological rigor and AI governance are aligned throughout the review lifecycle.

What’s New

1. AI Usage Transparency

• Visibility into AI usage across different stages (screening, extraction, reporting)

• Ability to review how AI contributes to decisions

• Supports documentation for audits and publications

2. AI Decision Validation

• Compare AI outputs with human decisions

• Built-in conflict analysis for Human vs AI decisions

• Helps assess accuracy, reliability, and trustworthiness of AI outputs

3. Tool Selection & Evaluation Support

• Enables users to evaluate:

  • Whether AI is appropriate for their review

  • If outputs meet required quality standards

• Aligns with RAISE 3 guidance on fit-for-purpose AI usage

4. Structured AI Workflow Integration

• AI is embedded across:

  • Title–Abstract screening

  • Full-text screening

  • Data extraction

  • Report generation

• Ensures AI usage remains consistent and traceable

5. Auditability & Reporting

• Track AI models used at each stage

• Maintain logs for:

  • Decisions

  • Outputs

  • Workflow steps

• Supports regulatory, publication, and governance requirements

6. Ethical & Responsible AI Use

• Encourages human oversight in all AI-assisted stages

• Ensures:

  • AI outputs are reviewed, not blindly accepted

  • Decisions remain human-controlled

Why This Matters

This update helps teams:

• Use AI tools responsibly and confidently

• Ensure methodological rigor and transparency

• Meet emerging global standards for AI in evidence synthesis

• Improve trust, reproducibility, and audit readiness

EasySLR now introduces Last Used tracking for organisation users, allowing Organisation Owners to view when each team member last accessed the platform.

Overview

The Last Used timestamp provides visibility into recent user activity across the organisation and projects, helping teams monitor engagement and manage user access more effectively.

What’s New

Last Used Date & Time

• View the most recent activity timestamp for each organisation user

Automatic Activity Tracking

• Timestamp updates based on user activity within the platform

• Updated periodically (approximately every hour)

Centralised Visibility

• Accessible at the organisation user level

• Enables quick identification of active and inactive users

Why This Matters

This feature helps teams:

• Track user activity across projects and organisation

• Identify inactive users for access management

• Reassign licenses or roles more efficiently

• Improve governance and user oversight

We’ve introduced the ability to replace existing PDFs during Bulk PDF Upload using AI, giving you greater control when uploading updated or new full-text files.

What’s New

Replace PDFs During Upload

• EasySLR now detects if a PDF already exists for a matched article during upload

• You can choose to replace the existing PDF directly from the upload table

Improved Batch Handling

• A new option allows you to select and replace multiple existing PDFs at once

• Clear indicators highlight:

  • New PDFs ready for import

  • PDFs that already exist

Better Visibility & Control

• A “PDF already exists” status is clearly displayed

• Replace actions can be reviewed and managed before final import

Why This Matters

This enhancement helps teams:

• Maintain a cleaner and more accurate document library

• Improve efficiency during bulk PDF uploads

Important Notes

• Replacement is optional and fully controlled by the user

• Existing PDFs will only be replaced when explicitly selected

• Manual upload remains available for unmatched PDFs

EasySLR now supports OpenAlex integration within the AI-powered TLR workflow, enabling automatic retrieval of articles from a broader, multidisciplinary database alongside PubMed.

What’s New

OpenAlex Article Import

• Automatically import articles from OpenAlex within the TLR workflow

• Access a large, open database of 250M+ scholarly works

• Seamlessly include OpenAlex results alongside existing sources

Expanded Literature Coverage

• Retrieve studies beyond traditional biomedical databases

• Capture multidisciplinary and cross-domain research

• Improve access to open-access and non-PubMed indexed studies

Improved Study Discovery

• Identify relevant studies that may not be available in PubMed

• Enhance coverage for topics spanning multiple disciplines

• Reduce the risk of missing key evidence

Seamless Integration

• OpenAlex results are automatically incorporated into the workflow

• No additional setup or manual import required

• Maintains a consistent experience across data sources 
  

Why This Matters

This update helps teams:

• Broaden their literature search beyond PubMed

• Capture a more diverse and comprehensive set of studies

With OpenAlex integration, EasySLR enables more comprehensive literature retrieval by combining traditional biomedical sources with a large, multidisciplinary open database ensuring better coverage and stronger research outcomes.

EasySLR has upgraded the PubMed Query Generation by introducing Search Query Validation, enabling users to not only build structured queries but also evaluate their effectiveness before moving to screening.

Overview

The Advanced Query Builder now combines structured query construction with built-in validation capabilities, allowing researchers to create, refine, and assess PubMed search strategies within a single interface.

In addition to real-time query building, users can now measure recall and verify whether key benchmark studies are captured—ensuring stronger methodological rigor and transparency

What’s New

Search Query Validation

• Validate PubMed search queries against known benchmark studies

• Check whether key identifiers (DOI, PMID, Title) are retrieved

• Identify missing studies before screening begins

• Measure recall (%) to evaluate search performance

Real-Time Query Preview

• View and edit the generated query instantly

• Combine search lines and adjust Boolean operators

• Maintain full control over query structure

Live Result Count

• Retrieve real-time result counts from PubMed

• Estimate screening volume and refine search scope

Built-In Filters

• Apply filters such as publication date, text availability, article type, and attributes

• Refine results before finalizing the search strategy

Search History Tracking

• Automatically save query, result count, recall, and timestamp

• Store up to 20 search iterations across sessions

• Enable comparison and documentation of search evolution 

Why This Matters

This enhancement helps teams:

• Validate search strategies before screening

• Ensure key studies are not missed

• Improve recall and overall search quality

• Maintain transparent and reproducible documentation

By combining query building and validation, EasySLR ensures that PubMed search strategies are both well-structured and methodologically robust before progressing to the next stage.

We’ve made it easier to work with RefIDs in EasySLR by introducing bulk copy-paste functionality directly from Excel.

What’s New

Bulk RefID Input via Copy-Paste
You can now copy a list of RefIDs from Excel and paste them directly into EasySLR- no need to enter them one by one.

Faster Article Selection & Actions
Quickly use pasted RefIDs to locate, filter, or take actions on multiple records at once.

Key Benefits

• Saves time by eliminating manual entry

• Simplifies bulk operations

• Enables quick identification of specific records

• Improves workflow efficiency during screening and management

How to Use

1. Copy the list of RefIDs from your Excel sheet

2. Go to the relevant section in EasySLR

3. Click on RefID filter and select Bulk IDs

4. Paste the RefIDs into the input field

5. Click Apply

6. You will get the list of entered RefIDs

Use Cases

• Filtering a predefined set of studies

• Revisiting specific articles for QC or validation

• Applying bulk actions on selected records 

We’ve made it easier and faster to access your generated files, right where the action happens.

What’s New

Direct access to download links within Background Tasks
All file download links are now available directly inside the Background Task.

This includes:

• ZIP files of PDF folders

• Article decision Excel files

• Other generated outputs

Key Benefits

• Faster access to files: No need to wait for email notifications

• Improved reliability: Download files directly even if there’s a delay in receiving emails

• Better visibility: Easily track and access files

How It Helps

If there’s any delay in receiving the download link via email, you can now simply navigate to the respective Background Task in history and download the file instantly.

Important Note: The download link has an expiry of 24 hours.

We’re excited to introduce AI-powered Mini Data Extraction at the Full-Text stage, enabling deeper and more accurate data capture when you need it most.

What’s New

AI-driven extraction from full-text articles
Capture detailed insights directly from full-text PDFs into your predefined Mini DE fields.

Seamless integration within Full-Text screening
AI suggestions are embedded within the workflow, allowing you to review and extract data without switching contexts.

Data Linked back to Source
Data Extracted by AI is linked back to the source of the information within the PDF

On-demand AI refresh (Rerun AI)
Update your fields and regenerate AI suggestions anytime for improved accuracy.

 Key Benefits

• Deeper data capture: Extract richer and more contextual information from full-text articles

• Reduced manual effort: Minimise time spent on repetitive data entry

• Reviewer control: Easily validate, edit, or override AI-generated outputs

• Flexible field support: Works across all Mini DE field types including Text, Multi-select, and Yes/No

Excel Export Enhancement

• AI-extracted Full-Text Mini DE data will be available in separate columns in Excel, along with screening decisions for easy review and analysis.

 How to Enable AI for Full-Text Mini DE

1. Go to Project Settings

2. Navigate to AI Copilot

3. Enable Full-Text Mini Data Extraction AI

4. Click Rerun AI to generate or refresh suggestions

Data Extracted by AI

Note: AI-generated outputs are designed to support reviewers. Please ensure all extracted data is reviewed and validated before finalising.

We’ve added AI into Mini Data Extraction at the Title–Abstract stage to help you capture structured insights earlier in your review workflow.

What’s New

• AI-powered Mini Data Extraction at Title–Abstract screening
  Automatically extracts data into predefined Mini DE fields while screening articles.

• Built directly into the screening workflow
  AI suggestions appear within the Mini DE fields—no separate setup required.

• Rerun AI functionality
  Easily refresh AI suggestions after updating your fields.

Key Benefits

• Faster early-stage data capture with minimal manual effort

• Full control to review, edit, or override AI-generated data

• Supports all Mini DE field types such as Text, Multi-select, and Yes/No fields.

• New Columns will be added to Excel containing Mini DE AI Data.

How to Enable AI for Title–Abstract Mini DE

1. Go to Project Settings

2. Navigate to AI Copilot

3. Enable Title–Abstract Mini Data Extraction AI

   

4. Click Rerun AI to generate or refresh suggestions

Note: AI-generated outputs are designed to assist reviewers. Please review and validate the extracted data before finalising.

A new dedicated DOI search filter has been added to the list of available search filters to make it easier to locate articles using their Digital Object Identifier (DOI).

Users can now quickly search for specific records by entering a DOI directly in the search filter panel. The system will instantly retrieve matching articles within the project.

Key Benefits:

• Quickly locate articles using DOI

• Improve article verification and traceability

• Simplify identification of duplicate or related records

• Speed up navigation within large projects
  

This enhancement provides a faster and more precise way to find articles during the review process.

A new pre-upload credit validation has been added to Bulk PDF Upload Using AI and Direct PDF Upload to Data Extraction (DE).

This enhancement ensures that the system verifies whether the organisation has sufficient AI credits before starting the upload process, preventing failed uploads and improving workflow transparency.

What’s New

Before initiating the upload process, EasySLR now checks if the organisation has enough AI credits to process the selected PDFs.

If the required credits are not available, the system immediately displays an error message and prevents the upload from starting.

This avoids unnecessary processing attempts and informs users about credit requirements in advance.

How It Works

1. Select PDFs for upload using:

   • Bulk PDF Upload Using AI, or

   • Direct PDF Upload to Data Extraction (DE).

2. The system calculates the total credits required based on the number of PDFs selected.

3. Before the upload begins, EasySLR verifies the organisation's available AI credit balance.

4. If sufficient credits are available:

   • The upload process proceeds normally.

5. If credits are insufficient:

   • The upload is blocked.

   • An error notification appears showing the required credits.

Error message example:

Error uploading PDFs
Insufficient credits. Required 310 credits.

Benefits

• Prevents failed uploads due to insufficient credits

• Provides clear visibility of credit requirements

• Improves system efficiency by avoiding unnecessary processing

• Enhances transparency in AI credit usage

We’ve introduced AI-Powered Smart Tags, Filters, and Visualisation to help teams automatically classify, analyse, and explore study characteristics across their projects.

AI Smart Tags automatically label articles using structured classifications based on:

• Population

• Intervention

• Outcome

• Country

• Study Design

• Evidence Level

These tags enable faster filtering and interactive evidence mapping.

^{What’s New}

AI Smart Tags

• Automatically classify articles using AI

• Display structured labels during article review

• Available across filtering and visualisation sections

Smart Tag Filters

• Filter articles using AI-generated classifications

• Combine multiple Smart Tag filters for refined analysis

• Use alongside manual filters (stage, reviewer, excluded, etc.)

Available Smart Tag filters:

• Population (AI)

• Intervention (AI)

• Outcome (AI)

• Country (AI)

• Study Design (AI)

• Evidence Level (AI)

Smart Tags Visualisation

• Interactive cross-tab visualisation of AI-generated tags

• Explore relationships between study characteristics

• Clickable cells to view associated articles

Visualisation formats include:

• Evidence Level View (color-coded)

• Heatmap View

• Bubble View

^{Where to Access}

Enable Smart Tags:

• Project Settings → AI Copilot

• Toggle on: Show AI smart tags when available while reviewing articles

Use Smart Tag Filters:

• Project Articles → Filters Section

Access Visualisation:

• Visualization

^{Why This Matters}

This enhancement helps teams:

• Map evidence faster without manual tagging

• Identify trends and evidence gaps

• Prioritise high-level evidence using AI classification

• Explore intervention–outcome relationships interactively

• Strengthen reporting and strategic decision-making

AI-Powered Smart Tags and Visualisation transform article classification into a structured, interactive, and insight-driven experience within EasySLR.

EasySLR now includes the Advanced Query Builder for PubMed, designed to help researchers create structured, transparent, and reproducible search strategies using a full-featured query interface. The tool replaces the basic PICOS editor with a more flexible system that enables real-time query construction, result validation, and structured documentation of search development.

^Overview

The Advanced Query Builder helps systematic reviewers develop and refine search strategies for PubMed using a structured interface inspired by the PubMed advanced search experience.

The tool allows users to build queries using the PICOS framework, preview query logic instantly, retrieve live result counts, and track multiple search iterations. This ensures the search development process remains traceable, reproducible, and audit-ready.

^{Key Capabilities}

Structured PICOS-Based Query Building

Organize search terms using the PICOS framework, including:

• Population

• Intervention

• Comparison

• Outcome

• Study Design

Reviewers can add terms under each category and construct queries using Boolean operators.

To add a term:

• Select the search field (e.g., Title/Abstract)

• Enter the search term

• Choose a Boolean operator (AND / OR)

• Click Add

The system automatically generates the structured query logic.

Real-Time Query Preview

The generated PubMed query appears instantly in the Query Box, allowing reviewers to:

• Edit the query manually

• Combine search lines

• Reference previous search steps (e.g., #1, #2)

• Adjust Boolean operators

This provides full flexibility while maintaining structured query development.

Live Result Count

The Count option retrieves the live number of results directly from PubMed.

This helps reviewers:

• Estimate screening workload

• Adjust search scope

• Refine Boolean logic

• Identify overly broad or restrictive queries

Run the Query in PubMed

The Open option allows users to run the generated query directly in PubMed.

This enables reviewers to:

• Validate results externally

• Export citations

• Confirm search behavior within PubMed.

Built-In Search Filters

The Filters Panel helps refine search results before finalizing the strategy.

Available filter options include:

• Publication Date (1 year, 5 years, 10 years, or custom range)

• Text Availability (Abstract, Free Full Text, Full Text)

• Article Type

• Article Attributes

Filters are organized into collapsible groups for easier navigation.

Reviewers should apply filters carefully to avoid unintended exclusions that may reduce recall.

Search History Tracking

Each time a query is executed, EasySLR automatically stores:

• Query string

• Result count

• Recall (when applicable)

• Timestamp

Key details:

• Up to 20 search iterations are stored

• Search history persists across sessions

• Data is stored securely within background job metadata

This enables reviewers to:

• Compare multiple search strategies

• Track improvements in recall

• Maintain documentation for audit and reporting.

Background Job Detail Panel

The job detail section is collapsible, allowing users to:

• Reduce interface clutter

• Focus on query results

• Review search performance more easily.

^{Best Practices}

To get the best results when using the Advanced Query Builder:

• Compare multiple search iterations before finalizing

• Check live result counts after modifying Boolean logic

• Broaden terms if recall appears too low

• Apply filters cautiously

• Run the query in PubMed for external verification

• Document the final query and result count in your protocol.

^Benefits

The Advanced Query Builder helps teams:

• Improve search transparency

• Strengthen methodological reproducibility

• Track search evolution automatically

• Reduce manual documentation effort

• Create protocol-ready search documentation

This feature ensures that PubMed search development within EasySLR remains structured, traceable, and methodologically rigorous.

The OpenAlex Query Generation tool in EasySLR allows you to search, validate, and import academic literature from OpenAlex, an open catalog of 250M+ scholarly works.

This expands EasySLR’s search capabilities beyond databases such as PubMed and ClinicalTrials.gov, enabling access to a broader, multidisciplinary source.

The tool combines AI-powered query generation, recall validation, dynamic filtering, and direct article import, helping teams build comprehensive literature searches efficiently.

What’s New

AI-Powered Query Generation

Users can generate OpenAlex search queries using AI by entering a research objective. The system structures the query using PICOS-based concepts and OpenAlex-compatible syntax.

Live Search Results

Generated queries return results in a live, paginated list displaying key article details such as title, authors, journal, publication year, citation count, and open access status.

Dynamic Filtering

Users can refine results using filters including:

• Work Type

• Open Access Status

• Language

• Retraction Status

• Publication Year

Query Validation

The tool includes built-in validation to measure recall and verify search coverage. Users can validate queries by entering identifiers such as DOI, PMID, or OpenAlex IDs to check whether key benchmark studies are captured.

Direct Article Import

Users can import records directly into their EasySLR project with multiple options:

• Select individual articles

• Bulk import (100 / 250 / 500 / 1000 records)

• Direct search-and-import of up to 10,000 results

Imported records are processed as background jobs with progress tracking.

Cross-Source Deduplication

Articles imported from OpenAlex include an OpenAlex ID, which is used as an additional signal during duplicate detection across PubMed imports, ClinicalTrials.gov imports, and existing project records.

^{Why This Matters}

This feature helps teams:

• Expand literature searches beyond traditional databases

• Access a broader multidisciplinary research catalog

• Validate search recall before importing studies

• Import large result sets efficiently

• Improve transparency and reproducibility of search strategies

OpenAlex Query Generation enables teams to conduct more comprehensive and flexible literature searches within EasySLR.

A new QC Status Filter has been introduced in the QC section to help users quickly categorize and review records based on their quality check progress.

This enhancement improves visibility, tracking, and reporting within the Quality Control workflow.

^{What’s New}

The QC Status filter allows you to:

• Identify records by QC progress

• Monitor pending quality checks

• Track overridden decisions

• Improve audit transparency

^{Available QC Status Options}

The filter includes:

• Done – Displays records where the quality check has been completed

• Decision Overridden – Displays records where the original reviewer decision was modified during QC

• Pending – Displays records awaiting quality check

^{Where to Access}

The QC Status Filter is available in:

• Title Abstract Screening → QC tab

• Full-Text Screening → QC tab

^{Why This Matters}

This enhancement helps teams:

• Track QC completion status more efficiently

• Identify overridden decisions quickly

• Monitor pending reviews without manual sorting

• Strengthen reporting and audit readiness

The QC Status Filter streamlines quality control management across screening stages in EasySLR.