As artificial intelligence (AI) continues to revolutionise various aspects of research, a crucial question emerges: Should AI replace humans in evidence synthesis? While AI offers remarkable speed and accuracy, the answer is nuanced. At EasySLR, we firmly believe that AI should augment human efforts rather than replace them entirely.

The Promise and Limitations of AI in Evidence Synthesis

The AI Advantage

AI has demonstrated impressive capabilities in processing vast amounts of data quickly and efficiently. In the context of systematic reviews, AI can significantly reduce the time spent on labour-intensive tasks such as:

• Duplicate detection

• Title and abstract screening

• Full-text screening

• Data extraction

• Quality assessment

These capabilities have been well-documented in literature in the past, with studies showing that AI can reduce the workload of systematic reviews by up to 50% (Tsafnat et al., 2014).

The Human Element: Irreplaceable Aspects

Despite AI's efficiency, several critical aspects of evidence synthesis remain firmly in the domain of human expertise:

• Contextual Understanding: AI excels at processing large volumes of data quickly, but it lacks the nuanced understanding that human researchers bring to the table. Evidence synthesis often requires interpreting complex studies, understanding subtle biases, and making judgment calls that are beyond the current capabilities of AI (O'Connor et al., 2019).

• Ethical Considerations: AI can inadvertently perpetuate biases present in its training data or oversimplify complex research findings. Without human oversight, these issues could lead to misleading conclusions, potentially affecting critical healthcare decisions (Char et al., 2018).

• Reliability of Sources: While AI can process data from countless studies, it cannot inherently distinguish between reliable and unreliable sources. Human expertise is essential for ensuring that only credible, peer-reviewed research is included in evidence synthesis (Borah et al., 2017).

• Complex Decision-Making: The process of evidence synthesis often involves making complex decisions about study inclusion, data interpretation, and the synthesis of findings across diverse studies. These tasks require a level of critical thinking and domain expertise that AI has not yet mastered (Thomas et al., 2017).

This view of AI as a tool to augment rather than replace human expertise is shared by leading health organizations. For instance, the National Institute for Health and Care Excellence (NICE) in the UK emphasizes that AI should be used judiciously to support and enhance decision-making, while maintaining human involvement in the process (NICE, 2024).

How EasySLR Augments Human Capabilities

At EasySLR, our AI Reviewer is designed to enhance human efforts rather than replace them. Here's how:

Efficiency Without Compromise

EasySLR streamlines resource intensive tasks, allowing researchers to focus on critical analyses and interpretations. By reducing the time spent on project management and automating certain steps, EasySLR enables quicker, yet thorough, systematic literature reviews. This approach aligns with recent research showing that AI-assisted reviews can maintain high accuracy while significantly reducing review time (Marshall et al., 2019).

Maintaining Human Oversight

Despite AI's efficiency, EasySLR ensures that final decisions remain in the hands of human experts. Our platform provides AI-generated insights, but humans validate these findings, ensuring accuracy and contextual relevance. This human-in-the-loop approach has been shown to be more effective than either human-only or AI-only methods in systematic reviews (Przybyła et al., 2018).

Collaborative Research

EasySLR fosters collaboration by offering tools for team-based reviews. Researchers can work together seamlessly, using AI to manage the workload while applying their collective expertise to ensure the quality and reliability of the review. This collaborative approach is crucial for maintaining the integrity and comprehensiveness of systematic reviews (Tricco et al., 2018).

Ethical Integrity

EasySLR is committed to upholding the highest ethical standards. Our AI tools are designed to support, not supersede, human judgement, ensuring that every piece of evidence is carefully considered before being included in the synthesis. This commitment aligns with ethical guidelines for AI in healthcare research (Nebeker et al., 2019).

Conclusion: The Future of Evidence Synthesis

While AI has the potential to improve the efficiency of evidence synthesis significantly, it cannot replace the critical thinking, ethical judgement, and contextual understanding that human researchers bring. At EasySLR, we see AI as a powerful tool that augments human capabilities, enabling researchers to conduct more efficient and accurate systematic literature reviews without compromising on quality or ethics.

By combining the best of both worlds—human expertise and AI efficiency—EasySLR represents the future of evidence synthesis. This synergistic approach not only accelerates the pace of research but also enhances its quality, potentially leading to more rapid advancements in healthcare and other critical fields.

As we move forward, it's crucial to continue refining the balance between AI assistance and human expertise in evidence synthesis. By doing so, we can harness the full potential of technology while ensuring that the fundamental principles of scientific inquiry and ethical research practices remain at the forefront of our endeavours.

References

• Borah, R., Brown, A. W., Capers, P. L., & Kaiser, K. A. (2017). Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry. BMJ Open, 7(2), e012545.

• Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. The New England Journal of Medicine, 378(11), 981-983.

• Marshall, I. J., Noel-Storr, A., Kuiper, J., Thomas, J., & Wallace, B. C. (2019). Machine learning for identifying randomized controlled trials: an evaluation and practitioner's guide. Research Synthesis Methods, 10(4), 534-545.

• Nebeker, C., Torous, J., & Bartlett Ellis, R. J. (2019). Building the case for actionable ethics in digital health research supported by artificial intelligence. BMC Medicine, 17(1), 137.

• National Institute for Health and Care Excellence. (2024). Use of AI in evidence generation: NICE position statement. Retrieved from https://www.nice.org.uk/about/what-we-do/our-research-work/use-of-ai-in-evidence-generation--nice-position-statement

• O'Connor, A. M., Tsafnat, G., Thomas, J., Glasziou, P., Gilbert, S. B., & Hutton, B. (2019). A question of trust: can we build an evidence base to gain trust in systematic review automation technologies? Systematic Reviews, 8(1), 143.

• Przybyła, P., Brockmeier, A. J., Kontonatsios, G., Le Pogam, M. A., McNaught, J., von Elm, E., ... & Ananiadou, S. (2018). Prioritising references for systematic reviews with RobotAnalyst: A user study. Research Synthesis Methods, 9(3), 470-488.

• Thomas, J., Noel-Storr, A., Marshall, I., Wallace, B., McDonald, S., Mavergames, C., ... & Elliott, J. (2017). Living systematic reviews: 2. Combining human and machine effort. Journal of Clinical Epidemiology, 91, 31-37.

• Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., ... & Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Annals of Internal Medicine, 169(7), 467-473.

• Tsafnat, G., Glasziou, P., Choong, M. K., Dunn, A., Galgani, F., & Coiera, E. (2014). Systematic review automation technologies. Systematic Reviews, 3(1), 74.