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arXiv AI By Xuyang Shen, Haoran Liu, Dongjin Song, Martin Renqiang Min

Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis

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The paper introduces Uncertainty-Guided Latent Diagnostic Trajectory Learning (LDTL), a framework that uses a planning LLM agent and a diagnostic LLM agent to model sequential clinical diagnosis under uncertainty. By treating diagnostic action sequences as latent paths and applying a posterior distribution that favors trajectories with higher diagnostic value, the approach encourages coherent evidence acquisition that progressively reduces uncertainty. Experiments on the MIMIC-CDM benchmark show that LDTL improves diagnostic accuracy while requiring fewer tests, and ablation studies confirm the importance of trajectory-level posterior alignment.

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