Guidelines and standard frameworks for artificial intelligence in medicine: a systematic review

Objectives The continuous integration of artificial intelligence (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 the quality of these guideline and summarizes ethical frameworks, best practices, and recommendations.Materials and Methods The Appraisal of Guidelines, Research, and Evaluation II tool was used to assess the quality of guidelines based on 6 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 of DERR1-10.2196/47105.Results The initial search resulted in 4975 studies from 2 databases and 7 studies from manual search. Eleven 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 rigor of guideline development. Well-established initiatives such as TRIPOD+AI, 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. The result also showed that the reproducibility, ethical, and environmental aspects of AI in medicine still need attention from both medical and AI communities.Discussion 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. This alignment is essential for fostering a cultural shift toward transparency and open science, which are pivotal milestone for sustainable digital health research.Conclusion This review evaluates the current reporting guidelines, discussing their advantages as well as challenges and limitations. As artificial intelligence (AI) continues to play an increasingly central role in health care, its safe and effective integration requires high-quality reporting guidelines that address the specific challenges of implementing AI in clinical settings. This systematic review evaluates the quality of existing AI reporting guidelines in medicine, focusing on their ethical considerations, best practices, and practical recommendations. Using the Appraisal of Guidelines, Research, and Evaluation II tool, we assessed the quality of the guidelines based on 6 key domains: scope, stakeholder involvement, development rigor, clarity, applicability, and editorial independence. While most guidelines performed well in areas such as scope and stakeholder involvement, significant variability was observed in their applicability and development rigor, reflecting the ongoing challenge of translating AI research into real-world health care settings. Additionally, the review highlighted the need for greater attention to reproducibility, ethics, and environmental impacts in medical AI research. Furthermore, the study underscores the importance of aligning guidelines with the Findable, Accessible, Interoperable, and Reusable principles to foster transparency, open science, and collaboration, which are crucial for advancing sustainable digital health research.