AI can help us to develop high-quality work, including systematic literature reviews (SLRs) for publication. In this article, published in the September edition of The MAP, the official newsletter of the International Society for Medical Publication Professionals, Polly Field, Richard White and Tomas Rees from Oxford PharmaGenesis provide guidance on how and when AI should be used to develop SLRs, drawing on experience as AI users and SLR experts.
In the article, they consider the potential applications of AI and the issues that should be considered when choosing an AI-based approach. Take a look at the full article here or read on for a quick summary.
AI can help with the scale, efficiency, quality and understanding of some SLRs.
AI can help across the full workflow of SLR development, if used correctly and with people to check and adjust the output. In the article, we look at approaches by stage of SLR, from early scoping, through searching and screening, to extracting data, assessing the quality of the studies and, finally, synthesizing and visualizing the findings.
However, before using an AI tool or platform, it needs to be critically appraised to understand the potential advantages and risks. We note important ethical, practical, business and legal considerations when using AI relating to confidentiality of information and the quality of the output.
We believe that a fundamental principle for the use of AI in SLRs is that the authors are responsible for the quality and accuracy of their work, whether developed entirely by themselves or with assistance from AI – the use of AI does not alter the author’s ownership of quality. Humans providing expert knowledge and oversight are crucial. AI should be considered an augmentation, not a human replacement. A rigorous methodology with multiple layers of human validation should be developed and agreed by all authors involved in the SLR. We also note other requirements when using AI, including aligning with relevant institutional and corporate policies, and being transparent in reporting its use.
We think that it is up to all of us to keep engaging with AI, to be alert to new approaches and applications, and to evaluate, upskill and start to use these new and evolving tools.
The team at Oxford PharmaGenesis have extensive experience in publishing SLRs and are now working with leading edge providers to explore and develop effective AI solutions for large and complex SLRs. Our agile approach means that we can identify the optimal solution for a wide range of SLR challenges, without being tied to a single platform technology. Talk to us to find out more!
