ArticleJapanese journal of radiology2024
Evaluating ChatGPT-4V in chest CT diagnostics: a critical image interpretation assessment.
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 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Language Models in Lung Cancer: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Evaluating ChatGPT-4o as an Educational Support Tool for the Emergency Management of Dental Trauma: Randomized Controlled Study Among Students.JMIR medical education · 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
- Artificial intelligence-enabled cytopathological interpretation of fluorescence microscopy images via a multimodal large language model.Quantitative biology (Beijing, China) · 2026Article
- Evaluating ChatGPT-5 for Detection of Barrett's Esophagus and Grading of Esophagitis: A Multiclass Endoscopic Image Analysis.JGH open : an open access journal of gastroenterology and hepatology · 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
- When AI joins the table: evaluating large language model performance in soft tissue sarcoma tumor board decisions.Journal of cancer research and clinical oncology · 2026Article
- Article
- Large language models in Chinese anesthesiology residency examinations: a comparative analysis of performance, reliability and clinical reasoning.BMC medical education · 2026Article
- Generative artificial intelligence in lung cancer care: current applications, challenges, and future directions.Frontiers in oncology · 2026Review
- Large Language Models in Radiologist-Patient Communication: A Narrative Review for Clinical Practice.Cureus · 2026Review
- AI-Assisted Diagnostic Evaluation of IHC in Forensic Pathology: A Comparative Study with Human Scoring.Diagnostics (Basel, Switzerland) · 2025Article
- Unveiling the Algorithm: The Role of Explainable Artificial Intelligence in Modern Surgery.Healthcare (Basel, Switzerland) · 2025Review
- Flexing ChatGPT-4o's Diagnostic Muscle: Detection of Fractures in the Ossifying Pediatric Elbow on Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- From Description to Diagnostics: Assessing AI's Capabilities in Forensic Gunshot Wound Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Chatbots in Radiology: Current Applications, Limitations and Future Directions of ChatGPT in Medical Imaging.Diagnostics (Basel, Switzerland) · 2025Review
- Assessing the value of artificial intelligence-based image analysis for pre-operative surgical planning of neck dissections and iENE detection in head and neck cancer patients.Discover oncology · 2025Article
- The Accuracy of ChatGPT-4o in Interpreting Chest and Abdominal X-Ray Images.Journal of personalized medicine · 2025Article
- Evaluating text and visual diagnostic capabilities of large language models on questions related to the Breast Imaging Reporting and Data System Atlas 5Diagnostic and interventional radiology (Ankara, Turkey) · 2025Observational
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8 authors.
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Abstract
purposeTo assess the diagnostic accuracy of ChatGPT-4V in interpreting a set of four chest CT slices for each case of COVID-19, non-small cell lung cancer (NSCLC), and control cases, thereby evaluating its potential as an AI tool in radiological diagnostics. MATERIALS AND
methodsIn this retrospective study, 60 CT scans from The Cancer Imaging Archive, covering COVID-19, NSCLC, and control cases were analyzed using ChatGPT-4V. A radiologist selected four CT slices from each scan for evaluation. ChatGPT-4V's interpretations were compared against the gold standard diagnoses and assessed by two radiologists. Statistical analyses focused on accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), along with an examination of the impact of pathology location and lobe involvement.
resultsChatGPT-4V showed an overall diagnostic accuracy of 56.76%. For NSCLC, sensitivity was 27.27% and specificity was 60.47%. In COVID-19 detection, sensitivity was 13.64% and specificity of 64.29%. For control cases, the sensitivity was 31.82%, with a specificity of 95.24%. The highest sensitivity (83.33%) was observed in cases involving all lung lobes. The chi-squared statistical analysis indicated significant differences in Sensitivity across categories and in relation to the location and lobar involvement of pathologies.
conclusionChatGPT-4V demonstrated variable diagnostic performance in chest CT interpretation, with notable proficiency in specific scenarios. This underscores the challenges of cross-modal AI models like ChatGPT-4V in radiology, pointing toward significant areas for improvement to ensure dependability. The study emphasizes the importance of enhancing these models for broader, more reliable medical use.
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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.