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This paper introduces Cros, a risk-constrained stopping layer for sequential clinical diagnosis agents that ensures finite-sample guarantees for diagnostic error and coverage, with evaluation on a benchmark derived from MIMIC.
This paper introduces MTDiag, a multi-turn diagnostic dialogue dataset for evaluating Large Language Models in realistic clinical diagnostic scenarios, addressing limitations of static QA benchmarks.
This paper proposes a multi-agent framework using deterministic orchestration and neuro-symbolic state tracking to mitigate premature diagnostic handoff and silent hallucinations in healthcare LLM applications.
A medical professional shares their positive experience using ChatGPT to assist in diagnostic pathology, demonstrating the AI's ability to provide accurate and detailed analysis comparable to a dermatopathologist.
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.