MedCLM: Learning to Localize and Reason via a CoT-Curriculum in Medical Vision-Language Models

Soo Yong Kim; Suin Cho; Vincent-Daniel Yun; Gyeongyeon Hwang

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arXiv · 2510.04477

MedCLM: Learning to Localize and Reason via a CoT-Curriculum in Medical Vision-Language Models

Soo Yong Kim·Suin Cho·Vincent-Daniel Yun·Gyeongyeon Hwang

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Abstract

Bridging clinical diagnostic reasoning with AI remains a central challenge in medical imaging. We introduce MedCLM, an automated pipeline that converts detection datasets into large-scale medical visual question answering (VQA) data with Chain-of-Thought (CoT) reasoning by linking lesion boxes to organ segmentation and structured rationales. These contextual signals enable medical vision-language models to generate question-answer pairs with step-by-step reasoning. To utilize this data effectively, we propose an Integrated CoT-Curriculum Strategy composed of an Easy stage with explicit lesion boxes for visual grounding, a Medium stage that encourages implicit localization, and a Hard stage for weakly supervised reasoning. Experimental results demonstrate that MedCLM attains state-of-the-art performance on several medical VQA benchmarks, providing a scalable framework for developing clinically aligned medical vision-language models.

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Soo Yong Kim, Suin Cho, Vincent-Daniel Yun, Gyeongyeon Hwang. 2025-10-06. MedCLM: Learning to Localize and Reason via a CoT-Curriculum in Medical Vision-Language Models. https://arxiv.org/abs/2510.04477

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