MedExAgent: Training LLMs for Clinical Diagnosis in Noisy Environments
MedExAgent is an innovative framework that views clinical diagnosis through the lens of a Partially Observable Markov Decision Process (POMDP). It involves three main actions: asking patients questions, ordering medical tests, and making diagnoses. This approach uses a detailed noise model with seven different patient noise categories to better mimic real-life clinical situations. By doing this, it aims to address the limitations of current medical language model benchmarks, which typically simplify diagnosis to basic Q&A or noise-free discussions. For more details on MedExAgent, check out the arXiv paper 2605.07058, which focuses on training LLM agents to handle interactive and uncertain clinical environments.
Key facts
- MedExAgent formalizes clinical diagnosis as a POMDP.
- Three action types: questioning, ordering exams, issuing diagnosis.
- Systematic noise model with seven patient noise types.
- Addresses oversimplification in existing medical LLM benchmarks.
- Described in arXiv paper 2605.07058.
- Focuses on interactive and uncertain clinical environments.
- Aims to train LLM agents for noisy clinical settings.
- Real-world diagnosis requires adapting to patient personas and incomplete information.
Entities
Institutions
- arXiv