ArticleJapanese journal of radiology2024
Diagnostic performances of GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro in "Diagnosis Please" cases.
Article in Japanese journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis 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
57 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Perception, assessment, and coaching: a systematic review and taxonomy of computer vision-based physical rehabilitation techniques.Frontiers in rehabilitation sciences · 2026Pooled it
- Large-scale evaluation of multimodal large language models for pneumothorax detection.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Diagnostic accuracy and consistency of ChatGPT-4o in radiology: influence of image, clinical data, and answer options on performance.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- GPT-4o-Powered Preanesthetic AI: Development and Validation Study.JMIR medical informatics · 2026Article
- Discordance Between Textual Reasoning and Visual Interpretation in Large Language Models for Low Back Pain: Cross-Sectional Quantitative Evaluation and Exploratory Multimodal Stress Test.JMIR medical informatics · 2026Article
- Stepwise Diagnostic Evaluation of Chinese Large Language Models: Comparative Study of Common and Rare Diseases.Journal of medical Internet research · 2026Article
- Benchmarking Multimodal Large Language Models for Cardiopulmonary Findings on Chest Radiographs: Sex-Stratified Discrimination and Operating Characteristics.Diagnostics (Basel, Switzerland) · 2026Article
- Zero-shot multimodal large language models underperform a domain-trained CNN baseline in pediatric wrist fracture detection.Scientific reports · 2026Article
- Artificial intelligence chatbots in endodontic education-Concepts and potential applications.International endodontic journal · 2026Review
- Performance of Multimodal Large Language Models in Detection and Position Assessment of Thoracic Devices on Chest Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Agentic GPT-5.0 system outperforms standard large language models and human experts in critical care clinical decision-making: a simulation study.BMC medical informatics and decision making · 2026Article
- Artificial Intelligence in Cancer Research: Modality Dependence and Limited Visual-Spatial Integration in Multimodal Large Language Models for Breast Cancer Histopathology.Life (Basel, Switzerland) · 2026Article
- Three large language models demonstrate competitive performance in Traditional Chinese Medicine national medical licensing examinations over two years.Scientific reports · 2026Article
- Performance of large language models in answering frequently-asked questions on celiac disease.Journal of pediatric gastroenterology and nutrition · 2026Article
- Artificial superintelligence alignment in healthcare.Japanese journal of radiology · 2026Review
- Insufficient reporting quality in large language model studies in the field of radiology.Insights into imaging · 2026Article
- Article
- ChatGPT-4o assists emergency physicians in enhancing diagnostic accuracy for fever of unknown origin: retrospective analysis.BMC emergency medicine · 2026Article
- Evaluation of large language models in a pulmonology outpatient clinic using structured clinical data and chest radiographs: a single-center prospective observational study.Frontiers in medicine · 2026Article
- AI-enabled multi-omics integration in colorectal cancer: from molecular stratification to clinical translation.Frontiers in cell and developmental biology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLarge language models (LLMs) are rapidly advancing and demonstrating high performance in understanding textual information, suggesting potential applications in interpreting patient histories and documented imaging findings. As LLMs continue to improve, their diagnostic abilities are expected to be enhanced further. However, there is a lack of comprehensive comparisons between LLMs from different manufacturers. In this study, we aimed to test the diagnostic performance of the three latest major LLMs (GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro) using Radiology Diagnosis Please Cases, a monthly diagnostic quiz series for radiology experts. MATERIALS AND
methodsClinical history and imaging findings, provided textually by the case submitters, were extracted from 324 quiz questions originating from Radiology Diagnosis Please cases published between 1998 and 2023. The top three differential diagnoses were generated by GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro, using their respective application programming interfaces. A comparative analysis of diagnostic performance among these three LLMs was conducted using Cochrane's Q and post hoc McNemar's tests.
resultsThe respective diagnostic accuracies of GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro for primary diagnosis were 41.0%, 54.0%, and 33.9%, which further improved to 49.4%, 62.0%, and 41.0%, when considering the accuracy of any of the top three differential diagnoses. Significant differences in the diagnostic performance were observed among all pairs of models.
conclusionClaude 3 Opus outperformed GPT-4o and Gemini 1.5 Pro in solving radiology quiz cases. These models appear capable of assisting radiologists when supplied with accurate evaluations and worded descriptions of imaging findings.
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.