Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Artificial Intelligence Extension

The investigators aim to develop the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Artificial Intelligence Extension (PRISMA-AI) guideline as a stand-alone extension of the PRISMA statement, modified to reflect the particular requirements for the reporting of AI and its related topics (namely machine learning, deep learning, neuronal networking) in systematic reviews.

With advances in artificial intelligence (AI) over the last two decades, enthusiasm and adoption of this technology in medicine have steadily increased. Yet despite the greater adoption of AI in medicine, the way such methodologies and results are reported varies widely and the readability of clinical studies utilizing AI can be challenging to the general clinician.

Systematic reviews of AI applications are an important area for which specific guidance is needed. An ongoing systematic review led by our team has shown that the number of systematic reviews on AI applications (with or without meta-analysis) is increasing dramatically over the time, yet the quality of reporting is still poor and heterogeneous, leading to inconsistencies in the reporting of informational details among individual studies. Consequently, the lack of these informational details may front problems for primary research and synthesis and potentially limits their usefulness for stakeholders interested in implementing AI or using the information in systematic reviews.

The criteria will derive from the consensus among multi-specialty experts (in each medical specialty) who have already published about AI applications in leading medical journals and the lead authors of PRISMA, STARD-AI, CONSORT-AI, SPIRIT-AI, TRIPOD-AI, PROBAST-AI, CLAIM-AI and DECIDE-AI to ensure that the criteria have global applicability in all the disciplines and for each type of study which involves the AI.

The proposed PRISMA-AI extension criteria focus on standardizing the reporting of methods and results for clinical studies utilizing AI. These criteria will reflect the most relevant technical details a data scientist requires for future reproducibility, yet they focus on the ability for the clinician reader to critically follow and ascertain the relevant outcomes of such studies.

The resultant PRISMA-AI extension will

1. help stakeholders interested in implementing AI or using AI-related information in systematic reviews
2. create a framework for reviewers that assess publications,
3. provide a tool for training researchers on Artificial Intelligence SR methodology
4. help end-users of the SR such as physicians and policymakers to better evaluate SR's validity and applicability in their decision-making process.

The success of the criteria will be seen in how manuscripts are written, how peer reviewers assess them, and finally, how the general readership is able to read and digest the published studies

Eligibility Criteria:
Inclusion Criteria:

* experts in the use AI technology in medicine
* experts in PRISMA
* leading authors of STARD-AI, CONSORT-AI, SPIRIT-AI, TRIPOD-AI, PROBAST-AI, CLAIM-AI and DECIDE-AI

Exclusion Criteria:

* Panelists who were not able to commit to all rounds of the modified Delphi process will be excluded