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Capabilities of GPT-5 on Multimodal Medical Reasoning

Created by
  • Haebom

Author

Shansong Wang, Mingzhe Hu, Qiang Li, Mojtaba Safari, Xiaofeng Yang

Outline

This study systematically evaluated the zero-shot chain-of-throat inference performance of GPT-5 as a multimodal inference engine for medical decision support in text-based and visual-based question answering tasks. We evaluated GPT-5, GPT-5-mini, GPT-5-nano, and GPT-4o-2024-11-20 on standardized datasets including MedQA, MedXpertQA, MMLU medical subset, USMLE self-assessment exam, and VQA-RAD. We found that GPT-5 outperformed all baseline models, achieving state-of-the-art accuracy on all QA benchmarks and demonstrating significant performance improvements in multimodal inference. Specifically, on MedXpertQA MM, GPT-5 improved the inference score by +29.26% and the comprehension score by +26.18% compared to GPT-4o, and outperformed licensed human experts by +24.23% and +29.40%, respectively. GPT-5 demonstrated the ability to integrate visual and textual cues to construct a coherent diagnostic inference chain and recommend appropriate high-risk interventions. These results suggest that GPT-5 performs beyond human and even expert levels on controlled multimodal inference benchmarks, providing valuable information for the design of future clinical decision support systems.

Takeaways, Limitations

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Takeaways:
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We demonstrated that GPT-5 outperformed human experts in multimodal reasoning in the medical field.
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By achieving excellent performance with only zero-shot learning, we present new possibilities for the development of medical decision support systems.
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By consistently demonstrating high performance across diverse medical datasets, we have confirmed the versatility and reliability of GPT-5.
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Provides Takeaways, which is important for the design and development of future clinical decision support systems.
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Limitations:
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This study used a limited benchmark dataset and may not fully reflect the complexity of real-world clinical settings.
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Further research is needed to explore the transparency and explainability of GPT-5's decision-making process.
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A more in-depth analysis of the model's bias and stability is needed.
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Additional performance validation in actual clinical environments is required.
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