ArticleEuropean radiology2025
Comparative analysis of GPT-4-based ChatGPT's diagnostic performance with radiologists using real-world radiology reports of brain tumors.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparing Diagnostic Accuracy of Clinical Professionals and Large Language Models: Systematic Review and Meta-Analysis.JMIR medical informatics · 2025Pooled it
- 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
- Evaluating expert decision-pattern alignment in endovascular planning for intracranial aneurysms using a multimodal large language model.Neuroradiology · 2026Article
- Large Language Models Versus Human Readers in CAD-RADS 2.0 Categorization of Coronary CT Angiography Reports.Journal of imaging informatics in medicine · 2026Article
- Bridging the gap: Translating fetal, infant, and toddler neuroimaging insights into clinical practice.Developmental cognitive neuroscience · 2026Review
- Prompt mechanisms in medical imaging: A comprehensive survey.Innovation (Cambridge (Mass.)) · 2026Review
- Large language models in neuro-oncology: promise, pitfalls, and the path forward.European radiology · 2026Article
- Estimation of histopathological types from breast MRI findings using a large language model.International journal of computer assisted radiology and surgery · 2026Article
- Artificial superintelligence alignment in healthcare.Japanese journal of radiology · 2026Review
- Impact of Large Language Model Assistance on Radiologists' Diagnostic Performance for Brain Tumors by Experience Level.Journal of clinical medicine · 2026Article
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage.NPJ digital medicine · 2026Article
- Integrating large language models (LLMs) into neuro-oncologic radiology: a retrospective feasibility assessment of GPT-4o for brain tumor diagnosis.Neuroradiology · 2026Article
- Intra-axial primary brain tumor differentiation: comparing large language models on structured MRI reports vs. radiologists on images.European radiology · 2026Article
- Reliability of Gemini 2.5 Pro, ChatGPT 4.1, DeepSeek V3, and Claude Opus 4 in generating standardized CMR protocols.European radiology experimental · 2026Article
- [Research progress of large language models in tumor diagnosis: applications in textual reports and medical imaging].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- Evaluation of large language models for PI-RADS score extraction from free-text prostate MRI reports: a comparative study with human readers.Frontiers in oncology · 2026Article
- Assessing GPT-4o in cataract surgery decision-making: appropriateness, consistency, and clinical implications.Frontiers in artificial intelligence · 2026Article
- Generative artificial intelligence as an aid in interpreting thyroid FNA cytopathological images: Are we there yet?Cancer cytopathology · 2026Article
- Large language models in patient education for brain tumors: opportunities, risks, and ethical considerations.Frontiers in oncology · 2026Review
- Diagnostic and interpretive gains from reasoning over conclusions with a large reasoning model in radiology.NPJ digital medicine · 2025Article
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Abstract
objectivesLarge language models like GPT-4 have demonstrated potential for diagnosis in radiology. Previous studies investigating this potential primarily utilized quizzes from academic journals. This study aimed to assess the diagnostic capabilities of GPT-4-based Chat Generative Pre-trained Transformer (ChatGPT) using actual clinical radiology reports of brain tumors and compare its performance with that of neuroradiologists and general radiologists.
methodsWe collected brain MRI reports written in Japanese from preoperative brain tumor patients at two institutions from January 2017 to December 2021. The MRI reports were translated into English by radiologists. GPT-4 and five radiologists were presented with the same textual findings from the reports and asked to suggest differential and final diagnoses. The pathological diagnosis of the excised tumor served as the ground truth. McNemar's test and Fisher's exact test were used for statistical analysis.
resultsIn a study analyzing 150 radiological reports, GPT-4 achieved a final diagnostic accuracy of 73%, while radiologists' accuracy ranged from 65 to 79%. GPT-4's final diagnostic accuracy using reports from neuroradiologists was higher at 80%, compared to 60% using those from general radiologists. In the realm of differential diagnoses, GPT-4's accuracy was 94%, while radiologists' fell between 73 and 89%. Notably, for these differential diagnoses, GPT-4's accuracy remained consistent whether reports were from neuroradiologists or general radiologists.
conclusionGPT-4 exhibited good diagnostic capability, comparable to neuroradiologists in differentiating brain tumors from MRI reports. GPT-4 can be a second opinion for neuroradiologists on final diagnoses and a guidance tool for general radiologists and residents. CLINICAL RELEVANCE STATEMENT: This study evaluated GPT-4-based ChatGPT's diagnostic capabilities using real-world clinical MRI reports from brain tumor cases, revealing that its accuracy in interpreting brain tumors from MRI findings is competitive with radiologists. KEY POINTS: We investigated the diagnostic accuracy of GPT-4 using real-world clinical MRI reports of brain tumors. GPT-4 achieved final and differential diagnostic accuracy that is comparable with neuroradiologists. GPT-4 has the potential to improve the diagnostic process in clinical radiology.
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