Doctorina MedBench: End-to-End Evaluation of Agent-Based Medical AI

TL;DR

Doctorina MedBench is a comprehensive framework for evaluating agent-based medical AI through realistic clinical dialogue simulations, assessing diagnostic accuracy, efficiency, and safety in a variety of scenarios.

Contribution

It introduces a novel simulation-based evaluation framework with multi-level testing and a new metric, D.O.T.S., for assessing medical AI performance in realistic interactions.

Findings

Simulation provides more realistic assessment of clinical competence.

The framework supports safety monitoring and regression testing.

Over 1,000 clinical cases with 750 diagnoses are included.

Abstract

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.