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- AI can enhance efficiency at every stage of SLR development, facilitating projects of scale that may previously have been unfeasible.<\/li>\n
- Use of AI in SLRs requires human oversight to ensure quality, transparency, reproducibility, and accuracy, with authors remaining accountable for their work.<\/li>\n<\/ul>\n
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<\/p>\nAs the demand for up-to-date systematic literature reviews (SLRs) grows, artificial intelligence (AI) is an increasingly appealing tool given its efficiency<\/a> and ability to manage a vast evidence base. In their article<\/a> for the International Society for Medical Publication Professionals (ISMPP<\/a>), Polly Field, Thomas Rees, and Richard White highlight the benefits of AI in SLRs and key considerations for its use.<\/p>\n
Benefits and pitfalls of AI <\/strong><\/p>\n
AI tools can streamline SLRs by analysing large datasets, summarising and grouping data, identifying patterns, and visualising findings \u2013 all in a fraction of the time it would take a team of researchers. However, careful attention must be given to how AI tools handle sensitive input data, including confidential content, copyrighted material, and personal information. Human validation remains essential to address potential inaccuracies, ‘hallucinations’, omissions, and bias produced by AI.<\/p>\n
When and how should AI be used?<\/strong><\/p>\n
Whether and how to use AI in SLRs depends on the context. AI can help to:<\/p>\n
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- frame research questions<\/li>\n
- optimise search strategies<\/li>\n
- screen studies<\/li>\n
- extract data<\/li>\n
- assess the quality of evidence, and<\/li>\n
- synthesise findings.<\/li>\n<\/ul>\n
Different AI tools<\/a> suit different stages, but the authors stress that all use of AI must adhere to strict principles of transparency, reproducibility, quality, and accuracy.<\/p>\n
Medical publication professionals should familiarise themselves with existing guidance<\/a> from the International Committee of Medical Journal Editors (ICMJE<\/a>) and individual journal policies<\/a>, as well as the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020<\/a> guidelines on disclosure of AI use. These policies expound the following principles:<\/p>\n
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- All authors remain fully accountable for the quality and accuracy of their work, including when AI is involved.<\/li>\n
- Transparency is critical \u2013 both the methods and acknowledgment sections must clearly document how and where AI was applied.<\/li>\n<\/ul>\n
The authors emphasise that human oversight is essential, ensuring AI supports rather than replaces expert judgement.<\/p>\n
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\u201cHuman oversight is essential, ensuring AI supports rather than replaces expert judgement.\u201d<\/p>\n<\/blockquote>\n
As AI embeds deeper into SLRs, the authors encourage medical publication professionals to explore the potential use of AI in their research, while adopting key principles to ensure robust, transparent, and high-quality reviews.<\/p>\n
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<\/a><\/p>\n