Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM): 2025 Updates

Abstract

Recent systematic reviews have raised concerns about the quality of reporting in studies evaluating the accuracy of large language models (LLMs) in medical applications. Incomplete and inconsistent reporting hampers the ability of reviewers and readers to assess study methodology, interpret results, and evaluate reproducibility. To address this issue, the MInimum reporting items for CLear Evaluation of Accuracy Reports of Large Language Models in healthcare (MI-CLEAR-LLM) checklist was developed. This article presents an extensively updated version. While the original version focused on proprietary LLMs accessed via web-based chatbot interfaces, the updated checklist incorporates considerations relevant to application programming interfaces and self-managed models, typically based on open-source LLMs. As before, the revised MI-CLEARLLM focuses on reporting practices specific to LLM accuracy evaluations: specifically, the reporting of how LLMs are specified, accessed, adapted, and applied in testing, with special attention to methodological factors that influence outputs. The checklist includes essential items across categories such as model identification, access mode, input data type, adaptation strategy, prompt optimization, prompt execution, stochasticity management, and test data independence. This article also presents reporting examples from the literature. Adoption of the updated MI-CLEAR-LLM can help ensure transparency in reporting and enable more accurate and meaningful evaluation of studies.