ArticleCureus2025
Evaluation of GPT-4 Accuracy in the Interpretation of Medical Imaging: Potential Benefits, Limitations, and the Future.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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0 citing papers in PubMed.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction The implementation of artificial intelligence (AI) in radiology as a medical decision support system has the potential to enhance diagnostic accuracy and improve patient outcomes. This retrospective study aimed to evaluate the diagnostic capabilities of GPT-4o in interpreting radiological imaging, specifically X-ray, CT, and MRI images, across various organ systems and disease types. Methods A total of 377 cases were collected and presented to GPT-4o with a standardized prompt and no clinical context. The responses were assessed by three independent raters using a five-point rating system. Results X-ray imaging exhibited a 2.21 times higher chance, on average, of being interpreted accurately compared to CT scans (odds ratio (OR): 2.21; 95% confidence interval (CI): 1.33 - 3.69), while pelvic imaging had a 6.25 times lower chance, on average, of being interpreted accurately when compared to images of the abdomen (OR: 0.16; 95% CI: 0.02 - 0.56). Additionally, neoplastic conditions had a 2.7 times lower chance, on average, of being interpreted accurately compared to bleeding conditions (OR: 0.37; 95% CI: 0.16 - 0.84). Conclusion A bimodal distribution of median ratings highlights an overreliance on comparability to prior image encounters and emphasizes the need to develop a systematic approach to image analysis. Future research should prioritize eliminating hallucination, establishing standardized evaluation criteria, and exploring methods to integrate visual and text-based data in a balanced manner. Additionally, efforts should be directed towards enhancing dataset diversity to improve the model's overall accuracy and generalizability.
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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.