[2506.13474] Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learning

Computer Science > Computation and Language arXiv:2506.13474 (cs) [Submitted on 16 Jun 2025 (v1), last revised 28 Feb 2026 (this version, v3)] Title:Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learning Authors:David Bani-Harouni, Chantal Pellegrini, Ege Özsoy, Nassir Navab, Matthias Keicher View a PDF of the paper titled Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learning, by David Bani-Harouni and 4 other authors View PDF HTML (experimental) Abstract:Clinical decision-making is a dynamic, interactive, and cyclic process where doctors have to repeatedly decide on which clinical action to perform and consider newly uncovered information for diagnosis and treatment. Large Language Models (LLMs) have the potential to support clinicians in this process, however, most applications of LLMs in clinical decision support suffer from one of two limitations: Either they assume the unrealistic scenario of immediate availability of all patient information and do not model the interactive and iterative investigation process, or they restrict themselves to the limited "out-of-the-box" capabilities of large pre-trained models without performing task-specific training. In contrast to this, we propose to model clinical decision-making for diagnosis with a hypothesis-driven uncertainty-aware language agent, LA-CDM, that converges towards a diagnosis via repeatedly requesting and interpreting relevant tests. Using a ...