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

Computer Science > Computation and Language [Submitted on 26 Mar 2026] Doctorina MedBench: End-to-End Evaluation of Agent-Based Medical AI Anna Kozlova, Stanislau Salavei, Pavel Satalkin, Hanna Plotnitskaya, Sergey Parfenyuk 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. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA) Cite as: arXiv:2603.25821 [cs.CL]   (or arXiv:2603.25821v1 [cs.CL] for this version)   https://doi.org/10.48550/arXiv.2603.25821 Focus to learn more Submission history From: Anna Kozlova [view email] [v1] Thu, 26 Mar 2026 18:38:25 UTC (3,784 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CL < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.AI cs.LG cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)