Doctorina MedBench: End-to-End Evaluation of Agent-Based Medical AI
This work provides a more realistic evaluation method for medical AI systems and physicians, addressing the limitation of standard question-answering benchmarks in assessing clinical dialogue skills.
Doctorina MedBench introduces a simulation-based evaluation framework for agent-based medical AI that models multi-step clinical dialogues, using the D.O.T.S. metric to assess clinical correctness and efficiency. The framework includes over 1,000 clinical cases covering 750+ diagnoses and demonstrates that simulated dialogue offers a more realistic assessment of clinical competence than traditional benchmarks.
We present Doctorina MedBench, a comprehensive evaluation framework for agent-based medical AI based on the simulation of realistic physician-patient interactions. Unlike traditional medical benchmarks that rely on solving standardized test questions, the proposed approach models a multi-step clinical dialogue in which either a physician or an AI system must collect medical history, analyze attached materials (including laboratory reports, images, and medical documents), formulate differential diagnoses, and provide personalized recommendations. System performance is evaluated using the D.O.T.S. metric, which consists of four components: Diagnosis, Observations/Investigations, Treatment, and Step Count, enabling assessment of both clinical correctness and dialogue efficiency. The system also incorporates a multi-level testing and quality monitoring architecture designed to detect model degradation during both development and deployment. The framework supports safety-oriented trap cases, category-based random sampling of clinical scenarios, and full regression testing. The dataset currently contains more than 1,000 clinical cases covering over 750 diagnoses. The universality of the evaluation metrics allows the framework to be used not only to assess medical AI systems, but also to evaluate physicians and support the development of clinical reasoning skills. Our results suggest that simulation of clinical dialogue may provide a more realistic assessment of clinical competence compared to traditional examination-style benchmarks.