ArticleScientific reports2024
Assessing ChatGPT 4.0's test performance and clinical diagnostic accuracy on USMLE STEP 2 CK and clinical case reports.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Performance of large language models in medical licensing examinations: a systematic review and meta-analysis.Journal of educational evaluation for health professions · 2025Pooled it
- Performance of ChatGPT, Claude, and AMBOSS on the European Board of Urology In-Service Assessment and Alignment With the European Association of Urology 2025 Guidelines: Comparative Study.JMIR formative research · 2026Article
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- SPOT THE BOT: A comparative quality assessment of AI-generated written instructions for lung ultrasound training.The ultrasound journal · 2026Article
- Large Language Model Performance and Clinical Reasoning Tasks.JAMA network open · 2026Article
- ChatGPT, Claude Sonnet, and Grok Display Similarly Low Rates of Accuracy in Identifying Image-Based Orthopaedic Sports Pathologies.Arthroscopy, sports medicine, and rehabilitation · 2026Article
- Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools.European journal of human genetics : EJHG · 2026Article
- Preparing for Pandemics with Large Language Models: An Evaluation of Sensitivity Across COVID-19, Zika, and Monkeypox Case Reports.Journal of medical systems · 2026Article
- Large language model detects previously undiagnosed heart failure with preserved ejection fraction in patients with metabolic-associated fatty liver disease: A multicenter cohort study.PLOS digital health · 2026Article
- Evaluating the performance of five large language models in generating patient educational content for pediatric cardiothoracic procedures: a comparative study.General thoracic and cardiovascular surgery · 2026Observational
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- Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Generative Artificial Intelligence in Healthcare: Automation Bias, Deskilling, and Cognitive Implications - A Systematic Review.Journal of healthcare leadership · 2026Review
- Diagnostic accuracy and repeatability of ChatGPT using textual and radiographic data in reversible pulpitis: a retrospective diagnostic study.Frontiers in bioinformatics · 2026Article
- Does the Quality and Readability of Information Related to Varicocele Obtained from ChatGPT 4.0 Remain Consistent Across Different Models of Inquiry?The world journal of men's health · 2026Article
- Enhancing Patient Understanding of Perianal Fistula MRI Findings Using ChatGPT: A Randomized, Single Centre Study.Diagnostics (Basel, Switzerland) · 2025Article
- AI Decision-Making Performance in Maternal-Fetal Medicine: Comparison of ChatGPT-4, Gemini, and Human Specialists in a Cross-Sectional Case-Based Study.Journal of clinical medicine · 2025Article
- Diagnostic Performance of Multimodal Large Language Models in the Analysis of Oral Pathology.Oral diseases · 2025Article
- A multi-query, multimodal, receiver-augmented solution to extract contemporary cardiology guideline information using large language models.European heart journal. Digital health · 2025Article
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6 authors.
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Abstract
While there is data assessing the test performance of artificial intelligence (AI) chatbots, including the Generative Pre-trained Transformer 4.0 (GPT 4) chatbot (ChatGPT 4.0), there is scarce data on its diagnostic accuracy of clinical cases. We assessed the large language model (LLM), ChatGPT 4.0, on its ability to answer questions from the United States Medical Licensing Exam (USMLE) Step 2, as well as its ability to generate a differential diagnosis based on corresponding clinical vignettes from published case reports. A total of 109 Step 2 Clinical Knowledge (CK) practice questions were inputted into both ChatGPT 3.5 and ChatGPT 4.0, asking ChatGPT to pick the correct answer. Compared to its previous version, ChatGPT 3.5, we found improved accuracy of ChatGPT 4.0 when answering these questions, from 47.7 to 87.2% (p = 0.035) respectively. Utilizing the topics tested on Step 2 CK questions, we additionally found 63 corresponding published case report vignettes and asked ChatGPT 4.0 to come up with its top three differential diagnosis. ChatGPT 4.0 accurately created a shortlist of differential diagnoses in 74.6% of the 63 case reports (74.6%). We analyzed ChatGPT 4.0's confidence in its diagnosis by asking it to rank its top three differentials from most to least likely. Out of the 47 correct diagnoses, 33 were the first (70.2%) on the differential diagnosis list, 11 were second (23.4%), and three were third (6.4%). Our study shows the continued iterative improvement in ChatGPT's ability to answer standardized USMLE questions accurately and provides insights into ChatGPT's clinical diagnostic accuracy.
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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.