ReviewKorean journal of radiology2025
Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM): 2025 Updates.
Review in Korean journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Reporting Quality of Large Language Model Studies: A Cross-Sectional Audit of High-Ranking Radiology and Medical Imaging Journals.Korean journal of radiology · 2026Article
- Artificial intelligence performance in the emergency medicine subspecialty examination conducted in Türkiye.Medicine · 2026Article
- Test-Time Compute and Budget ("Reasoning Effort"): An Underrecognized Methodologic Variable in Medical Large Language Model Research.Korean journal of radiology · 2026Article
- From study design to executable code: automating target trial emulation with large language models.JAMIA open · 2026Article
- Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review.JMIR cancer · 2026Article
- Performance of DeepSeek-R1 and ChatGPT-5 in the Generation of North American Spine Society Clinical Guidelines for Adult Vertebral Compression Fractures: Comparative Study.Journal of medical Internet research · 2026Article
- Response to "Evaluating Multimodal Large Language Models in Neuroradiology: Methodological Considerations".Korean journal of radiology · 2026Article
- Agentic Artificial Intelligence for the Automated Generation of Accurate Summary Podcasts of Radiology Research Papers.Korean journal of radiology · 2026Article
- Review
- Do General-Purpose Multimodal Large Language Models Really See Radiologic Images or Rely on Text?Korean journal of radiology · 2026Article
- Improving non-expert performance in musculoskeletal MRI protocoling through a large language model.Scientific reports · 2026Article
- Evaluating the Accuracy and Diagnostic Reasoning of Multimodal Large Language Models in Interpreting Neuroradiology Cases FromKorean journal of radiology · 2026Article
- Radiology Research and Publishing Across 2025-2026: Perspectives FromKorean journal of radiology · 2026Article
- Large language models for ophthalmic examination understanding: from information extraction to clinical decision support.Frontiers in medicine · 2026Review
- A centralized decision-making support consultation response for fertility preservation in breast cancer patients: benchmark performance of generative large language models in terms of reliability and readability.Frontiers in public health · 2026Article
- Benchmarking publicly accessible large language models for English-language patient-facing acute pancreatitis information: a cross-sectional study of quality, transparency, and readability.Frontiers in public health · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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-CLEAR-LLM 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.