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Computer Science > Computer Vision and Pattern Recognition

arXiv:2403.12046 (cs)
[Submitted on 14 Nov 2023]

Title:GPT-4V(ision) Unsuitable for Clinical Care and Education: A
Clinician-Evaluated Assessment

Authors:Senthujan Senkaiahliyan, Augustin Toma, Jun Ma, An-Wen Chan,
Andrew Ha, Kevin R. An, Hrishikesh Suresh, Barry Rubin, Bo Wang
View a PDF of the paper titled GPT-4V(ision) Unsuitable for Clinical
Care and Education: A Clinician-Evaluated Assessment, by Senthujan
Senkaiahliyan and 8 other authors
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    Abstract: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.

Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2403.12046 [cs.CV]
          (or arXiv:2403.12046v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2403.12046
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Jun Ma [view email]
[v1] Tue, 14 Nov 2023 17:06:09 UTC (5,281 KB)
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    View a PDF of the paper titled GPT-4V(ision) Unsuitable for
    Clinical Care and Education: A Clinician-Evaluated Assessment, by
    Senthujan Senkaiahliyan and 8 other authors
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