Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V

• URL: http://arxiv.org/abs/2310.19061v1
• Date: Sun, 29 Oct 2023 16:26:28 GMT
• Title: Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V
• Authors: Zhiling Yan, Kai Zhang, Rong Zhou, Lifang He, Xiang Li, Lichao Sun
• Abstract summary: We critically evaluate the capabilities of the state-of-the-art multimodal large language model, GPT-4 with Vision (GPT-4V) Our experiments thoroughly assess GPT-4V's proficiency in answering questions paired with images using both pathology and radiology datasets. The experiments with accuracy score conclude that the current version of GPT-4V is not recommended for real-world diagnostics.
• Score: 20.84152508192388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i.e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task. Our experiments thoroughly assess GPT-4V's proficiency in answering questions paired with images using both pathology and radiology datasets from 11 modalities (e.g. Microscopy, Dermoscopy, X-ray, CT, etc.) and fifteen objects of interests (brain, liver, lung, etc.). Our datasets encompass a comprehensive range of medical inquiries, including sixteen distinct question types. Throughout our evaluations, we devised textual prompts for GPT-4V, directing it to synergize visual and textual information. The experiments with accuracy score conclude that the current version of GPT-4V is not recommended for real-world diagnostics due to its unreliable and suboptimal accuracy in responding to diagnostic medical questions. In addition, we delineate seven unique facets of GPT-4V's behavior in medical VQA, highlighting its constraints within this complex arena. The complete details of our evaluation cases are accessible at https://github.com/ZhilingYan/GPT4V-Medical-Report.

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