ArticleNeuroradiology2024
Accuracy of ChatGPT generated diagnosis from patient's medical history and imaging findings in neuroradiology cases.
Article in Neuroradiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed.
- Human-AI collaboration in large language model-assisted brain MRI differential diagnosis: a usability study.European radiology · 2025Trial
- Prospective quantitative analysis of hyperparameter and input optimization in GPT-5: comparative contribution to radiologist performance in abdominal radiology.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Predicting molecular types of adult-type diffuse gliomas based on MRI reports with large language models.European radiology · 2026Article
- Integrating Generative AI Into Patient-Centered Clinical Decision Support: Viewpoint on Research and Practice Considerations.Journal of medical Internet research · 2026Article
- ChatGPT in precision medicine.APL bioengineering · 2026Review
- Impact of Large Language Model Assistance on Radiologists' Diagnostic Performance for Brain Tumors by Experience Level.Journal of clinical medicine · 2026Article
- Can AI write your code? A case study of chatgpt's statistical coding capabilities for quantitative research.Health economics review · 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
- Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.JMIR medical informatics · 2025Article
- Virtual case reasoning and AI-assisted diagnostic instruction: an empirical study based on body interact and large language models.BMC medical education · 2025Article
- Comparative performance of large language models in structuring head CT radiology reports: multi-institutional validation study in Japan.Japanese journal of radiology · 2025Article
- An Australasian survey on the use of ChatGPT and other large language models in medical physics.Physical and engineering sciences in medicine · 2025Article
- Assessing the Accuracy and Completeness of AI-Generated Dental Responses: An Evaluation of the Chat-GPT Model.Healthcare (Basel, Switzerland) · 2025Article
- Accuracy of large language models in generating differential diagnosis from clinical presentation and imaging findings in pediatric cases.Pediatric radiology · 2025Article
- Cancer type, stage and prognosis assessment from pathology reports using LLMs.Scientific reports · 2025Article
- Large language model integrations in cancer decision-making: a systematic review and meta-analysis.NPJ digital medicine · 2025Article
- Evaluation of radiology residents' reporting skills using large language models: an observational study.Japanese journal of radiology · 2025Observational
- Chatbots in Radiology: Current Applications, Limitations and Future Directions of ChatGPT in Medical Imaging.Diagnostics (Basel, Switzerland) · 2025Review
- Influence of prior probability information on large language model performance in radiological diagnosis.Japanese journal of radiology · 2025Article
- The Accuracy of ChatGPT-4o in Interpreting Chest and Abdominal X-Ray Images.Journal of personalized medicine · 2025Article
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10 authors.
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Abstract
purposeThe noteworthy performance of Chat Generative Pre-trained Transformer (ChatGPT), an artificial intelligence text generation model based on the GPT-4 architecture, has been demonstrated in various fields; however, its potential applications in neuroradiology remain unexplored. This study aimed to evaluate the diagnostic performance of GPT-4 based ChatGPT in neuroradiology.
methodsWe collected 100 consecutive "Case of the Week" cases from the American Journal of Neuroradiology between October 2021 and September 2023. ChatGPT generated a diagnosis from patient's medical history and imaging findings for each case. Then the diagnostic accuracy rate was determined using the published ground truth. Each case was categorized by anatomical location (brain, spine, and head & neck), and brain cases were further divided into central nervous system (CNS) tumor and non-CNS tumor groups. Fisher's exact test was conducted to compare the accuracy rates among the three anatomical locations, as well as between the CNS tumor and non-CNS tumor groups.
resultsChatGPT achieved a diagnostic accuracy rate of 50% (50/100 cases). There were no significant differences between the accuracy rates of the three anatomical locations (p = 0.89). The accuracy rate was significantly lower for the CNS tumor group compared to the non-CNS tumor group in the brain cases (16% [3/19] vs. 62% [36/58], p < 0.001).
conclusionThis study demonstrated the diagnostic performance of ChatGPT in neuroradiology. ChatGPT's diagnostic accuracy varied depending on disease etiologies, and its diagnostic accuracy was significantly lower in CNS tumors compared to non-CNS tumors.
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