Guidelines and Standard Frameworks for Artificial Intelligence in Medicine: A Systematic Review

Abstract

AbstractA growing volume of evidence marks the potential of Artificial Intelligence (AI) in medicine, in improving diagnostic accuracy, clinical decision support, risk/event prediction, drug discovery, and patient management. However, the continuous integration of AI into clinical settings requires the development of up-to-date and robust guidelines and standard frameworks that consider the evolving challenges of AI implementation in medicine. This review evaluates these guidelines’ quality and summarizes ethical frameworks, best practices, and recommendations.The Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool was used to assess the quality of guidelines based on six domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence. The protocol of this review including the eligibility criteria, the search strategy data extraction sheet and methods, was published prior to the actual review with International Registered Report Identifier (IRRID) of(DERR1-10.2196/47105).The initial search resulted in 4,975 studies from two databases and five studies from manual search. Nine articles were selected for data extraction based on the eligibility criteria. We found that while guidelines generally excel in scope, purpose, and editorial independence, there is significant variability in applicability and the rigour of guideline development. Well-established initiatives such as DECIDE-AI, SPIRIT-AI, and CONSORT-AI have shown high quality, particularly in terms of stakeholder involvement. However, applicability remains a prominent challenge among the guidelines. We conclude that the reproducibility, ethical and environmental aspects of AI in medicine still need attention from both medical and AI communities. This review emphasizes the crucial need for high-quality guidelines and opens a new avenue in evaluating guidelines themselves. Our work highlights the need for working toward the development of integrated and comprehensive reporting guidelines that adhere to the principles of Findability, Accessibility, Interoperability and Reusability (FAIR). This alignment is essential for fostering a cultural shift towards transparency and open science, which are pivotal milestone for sustainable digital health research.