The authors formalize clinical diagnosis as a Partially Observable Markov Decision Process (POMDP) where the agent can ask questions, order exams (as tool calls), or issue a diagnosis—all under systematic noise (seven patient noise types, three exam noise types). Existing medical LLM benchmarks simplify this to single-turn QA or noise-free conversations, missing the interactive, uncertain nature of real diagnosis. They train MedExAgent via supervised fine-tuning on synthetic Calgary-Cambridge-style clinical interviews, then apply DAPO (a policy optimization method) to maximize a composite reward: diagnostic accuracy, tool-call quality, and exam cost (financial + patient discomfort). MedExAgent matches larger models' diagnostic performance while maintaining cost-efficient exam strategies.
Main takeaways:
- Real clinical diagnosis involves questioning, exam ordering, and diagnosis under noisy, incomplete information—existing benchmarks ignore this interactivity.
- MedExAgent formalizes diagnosis as a POMDP with three action types and a systematic noise model (e.g., vague patient answers, false-negative test results).
- Two-stage training: first supervised fine-tuning on synthetic Calgary-Cambridge interviews, then DAPO to optimize accuracy + tool quality + exam cost.
- Achieves diagnostic performance comparable to larger models while keeping exam costs low.
- Demonstrates that training on structured interview formats (Calgary-Cambridge) transfers to noisy, interactive diagnosis tasks.