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Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning

Created by
  • Haebom

Author

Ruiqi Wu, Yuang Yao, Tengfei Ma, Chenran Zhang, Na Su, Tao Zhou, Geng Chen, Wen Fan, Yi Zhou

Outline

This paper proposes MM-Retinal-Reason, the first multimodal ophthalmology dataset capable of performing various types of inference (basic and complex) in the ophthalmology domain, and a multimodal inference model, OphthaReason, based on it. OphthaReason demonstrates a step-by-step inference process and flexibly adapts to both basic and complex inference tasks using the Uncertainty-Aware Dynamic Reasoning (UADT) technique. Experimental results show that OphthaReason achieves at least 15% performance improvement over existing models (general-purpose MLLM, medical MLLM, reinforcement learning-based medical MLLM, and ophthalmology MLLM).

Takeaways, Limitations

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Takeaways:
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We present a novel multimodal dataset and model capable of performing complex inference processes required for ophthalmic diagnosis.
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We demonstrated that a dynamic reasoning technique considering uncertainty (UADT) can effectively address various inference tasks.
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It can contribute to the development of ophthalmic diagnosis support systems by achieving significant performance improvements compared to existing models.
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Limitations:
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There is a lack of specific information about the size and diversity of the MM-Retinal-Reason dataset.
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Further research is needed to determine the generalizability of the UADT technique and its extendibility to other medical fields.
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There is a lack of performance validation in actual clinical environments.
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