MedDDC-Eval: Diagnosis-Decoupled Evaluation of Multi-Turn Medical Consultation Agents

TL;DR - MedDDC-Eval evaluates multi-turn medical consultation agents by separating history-taking quality from diagnosis generation. This enables more controlled comparisons and supports training agents to gather diagnostically useful evidence efficiently.

• A shared frozen diagnostic reader scores policy-elicited histories, reducing confounding from policy-specific diagnosis generation.
• Changing only the reader shifted diagnosis F1 by 2.2–19.0 points and reversed 18%–36% of policy rankings.
• The framework measures diagnostic usefulness, information acquisition, and trajectory efficiency across Record and Dialogue splits.
• GRPO post-training of Qwen3-32B improved total scores by 9.7 and 4.6 points on the two held-out splits.