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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.12046 |
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| _version_ | 1866917617496752128 |
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| author | Senkaiahliyan, Senthujan Toma, Augustin Ma, Jun Chan, An-Wen Ha, Andrew An, Kevin R. Suresh, Hrishikesh Rubin, Barry Wang, Bo |
| author_facet | Senkaiahliyan, Senthujan Toma, Augustin Ma, Jun Chan, An-Wen Ha, Andrew An, Kevin R. Suresh, Hrishikesh Rubin, Barry Wang, Bo |
| contents | OpenAI's large multimodal model, GPT-4V(ision), was recently developed for general image interpretation. However, less is known about its capabilities with medical image interpretation and diagnosis. Board-certified physicians and senior residents assessed GPT-4V's proficiency across a range of medical conditions using imaging modalities such as CT scans, MRIs, ECGs, and clinical photographs. Although GPT-4V is able to identify and explain medical images, its diagnostic accuracy and clinical decision-making abilities are poor, posing risks to patient safety. Despite the potential that large language models may have in enhancing medical education and delivery, the current limitations of GPT-4V in interpreting medical images reinforces the importance of appropriate caution when using it for clinical decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12046 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | GPT-4V(ision) Unsuitable for Clinical Care and Education: A Clinician-Evaluated Assessment Senkaiahliyan, Senthujan Toma, Augustin Ma, Jun Chan, An-Wen Ha, Andrew An, Kevin R. Suresh, Hrishikesh Rubin, Barry Wang, Bo Computer Vision and Pattern Recognition OpenAI's large multimodal model, GPT-4V(ision), was recently developed for general image interpretation. However, less is known about its capabilities with medical image interpretation and diagnosis. Board-certified physicians and senior residents assessed GPT-4V's proficiency across a range of medical conditions using imaging modalities such as CT scans, MRIs, ECGs, and clinical photographs. Although GPT-4V is able to identify and explain medical images, its diagnostic accuracy and clinical decision-making abilities are poor, posing risks to patient safety. Despite the potential that large language models may have in enhancing medical education and delivery, the current limitations of GPT-4V in interpreting medical images reinforces the importance of appropriate caution when using it for clinical decision-making. |
| title | GPT-4V(ision) Unsuitable for Clinical Care and Education: A Clinician-Evaluated Assessment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.12046 |