ReviewNature medicine2025
The TRIPOD-LLM reporting guideline for studies using large language models.
Review in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 304 papers, 3 of them syntheses that pooled it.
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
304 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2026Pooled it
- Improvement of Clinical Practice Guideline Appraisal by Human Experts and AI Agents by Using Structured Guidance: Systematic Review, Meta-Analysis, and Validation Study.Journal of medical Internet research · 2026Pooled it
- LLM-assisted systematic review of large language models in clinical medicine.Nature medicine · 2026Pooled it
- Evaluation of a cornea-specialized large language model for diagnostic and management accuracy in complex corneal cases.International ophthalmology · 2026Trial
- Moving towards acceleration with accountability: a conceptual framework for AI-assisted systematic reviews.Global epidemiology · 2026Article
- The daily dose: Early usability of an LLM tool for patient summaries and trial matching in radiation oncology.Clinical and translational radiation oncology · 2026Article
- Artificial intelligence in orthodontics: clinical maturity, evidence gaps, and emerging applications-a narrative review.The Saudi dental journal · 2026Review
- Spatial Validation of General-Purpose Multimodal AI-Generated Target Regions in Peripheral Nerve Block Ultrasound.Journal of imaging informatics in medicine · 2026Article
- AI-Based Structured Information Extraction From Synthetic Nursing Handover Transcripts: Comparative Evaluation of Large Language Models.JMIR nursing · 2026Article
- Periodontitis Risk Assessment and Prevention Planning: Comparative Study of Multimodal Large Language Models and Periodontist Evaluations.Journal of medical Internet research · 2026Article
- Cultural bias in large language models' ability to follow neuroradiology guidelines.European radiology · 2026Article
- Evaluating Injection Laryngoplasty Skills Using a Foundation Model: A Feasibility Study.The Laryngoscope · 2026Article
- Large Language Models and Retrieval-Augmented Platforms for the Diagnosis and Management of Periodontal Diseases: A Blinded Expert-Rated Comparative Study of 11 Systems.Journal of clinical periodontology · 2026Article
- Artificial Intelligence-Generated Electronic Medical Record Summarization in Breast Surgical Oncology.Annals of surgical oncology · 2026Article
- Accountability for large language models in health care.Bulletin of the World Health Organization · 2026Article
- The consensus-based CINEX guideline for reporting clinical information extraction studies.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Multicenter Evaluation of Large Language Models Versus Hepatologists for Prognostic Prediction in Drug-Induced Liver Injury.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Verification of the impact of differences between objective and subjective evaluation methods on the interpretation of artificial intelligence systems generating mammogram reports using vision-language model.Japanese journal of radiology · 2026Article
- An Evidence-Based Framework for Patient-Facing Artificial Intelligence Integration in the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- Diagnostic performance of large language models for discrepancy detection in non-English resident-authored abdominal CT and MRI reports: a Turkish-language evaluation.Abdominal radiology (New York) · 2026Article
244 more citing papers are in PubMed but not listed here.
Corrections and comments
- Update of
Authors and funding
25 authors.
Funding
Abstract
Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion. The guidelines introduce a modular format accommodating various LLM research designs and tasks, with 14 main items and 32 subitems applicable across all categories. Developed through an expedited Delphi process and expert consensus, TRIPOD-LLM emphasizes transparency, human oversight and task-specific performance reporting. We also introduce an interactive website ( https://tripod-llm.vercel.app/ ) facilitating easy guideline completion and PDF generation for submission. As a living document, TRIPOD-LLM will evolve with the field, aiming to enhance the quality, reproducibility and clinical applicability of LLM research in healthcare through comprehensive reporting.
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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.