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This paper introduces MedAction, a framework for training LLMs on active, multi-turn clinical diagnosis by simulating iterative test ordering and hypothesis updates. It presents a new dataset, MedAction-32K, and demonstrates state-of-the-art performance for open-source models on medical benchmarks.
The paper introduces MedExAgent, a framework that formalizes clinical diagnosis as a Partially Observable Markov Decision Process (POMDP) to handle noisy and incomplete information. It proposes a two-stage training pipeline combining supervised finetuning and reinforcement learning to improve diagnostic accuracy and cost-efficiency in medical LLMs.