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  1. Home
  3. Lack Of Methodological Rigor And Limited Coverage Of Generative Artificial Intelligence In Existing Artificial Intelligence Reporting Guidelines: A Scoping Review.
  1. Home
  3. Lack Of Methodological Rigor And Limited Coverage Of Generative Artificial Intelligence In Existing Artificial Intelligence Reporting Guidelines: A Scoping Review.

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Lack of methodological rigor and limited coverage of generative artificial intelligence in existing artificial

Xufei Luo1, Bingyi Wang1, Qianling Shi2

  • 1Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China; Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China; World Health Organization Collaboration Center for Guideline Implementation and Knowledge Translation, Lanzhou, China; Institute of Health Data Science, Lanzhou University, Lanzhou, China; Key Laboratory of Evidence Based Medicine of Gansu Province, Lanzhou University, Lanzhou, China.

Journal of Clinical Epidemiology
|July 20, 2025

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
Artificial intelligenceGenerative artificial intelligenceLarge language modelsMethodological qualityReporting guidelinesScoping review

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Existing medical artificial intelligence (AI) reporting guidelines lack rigor and do not adequately cover generative AI (GAI) tools like large language models (LLMs). Updated guidelines are needed to improve development processes and focus on emerging AI technologies.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Health Research Methodology

Background:

  • Reporting guidelines are crucial for the transparent and reproducible development of artificial intelligence (AI) in medicine.
  • Existing guidelines may not adequately address the unique challenges posed by emerging AI technologies, such as generative AI (GAI).

Purpose of the Study:

  • To systematically review the development methods, scope, and limitations of current AI reporting guidelines in medicine.
  • To assess the applicability of existing guidelines to generative AI (GAI) tools, including large language models (LLMs).

Main Methods:

  • A scoping review was conducted adhering to the PRISMA extension for Scoping Reviews (PRISMA-ScR).
  • Searches were performed across five databases (MEDLINE, EQUATOR Network, CNKI, FAIRsharing, Google Scholar) up to December 31, 2024.
  • Data extraction and analysis were performed by two independent reviewers, with discrepancies resolved by a third reviewer.

    • Main Results:

    • Sixty-eight AI reporting guidelines were identified, with only 7.4% specifically addressing GAI/LLMs.
    • Methodological rigor was limited: 39.7% described development processes, 42.6% involved multidisciplinary experts, and 33.8% followed EQUATOR recommendations.
    • GAI-specific guidelines showed insufficient coverage and transparency, with significant overlap in areas like medical imaging.

    Conclusions:

    • Current AI reporting guidelines in medicine exhibit suboptimal methodological rigor, redundancy, and inadequate coverage of GAI.
    • Future guidelines must emphasize standardized development, multidisciplinary input, and a broader scope to encompass advanced AI technologies like LLMs.