3MDBench: Medical Multimodal Multi-agent Dialogue Benchmark

• URL: http://arxiv.org/abs/2504.13861v1
• Date: Wed, 26 Mar 2025 07:32:05 GMT
• Title: 3MDBench: Medical Multimodal Multi-agent Dialogue Benchmark
• Authors: Ivan Sviridov, Amina Miftakhova, Artemiy Tereshchenko, Galina Zubkova, Pavel Blinov, Andrey Savchenko,
• Abstract summary: Large Vision-Language Models (LVLMs) are being explored for applications in telemedicine, yet their ability to engage with diverse patient behaviors remains underexplored.<n>We introduce 3MDBench, an open-source evaluation framework designed to assess LLM-driven medical consultations.<n>The benchmark integrates textual and image-based patient data across 34 common diagnoses, mirroring real-world telemedicine interactions.
• Score: 0.29987253996125257
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Vision-Language Models (LVLMs) are increasingly being explored for applications in telemedicine, yet their ability to engage with diverse patient behaviors remains underexplored. We introduce 3MDBench (Medical Multimodal Multi-agent Dialogue Benchmark), an open-source evaluation framework designed to assess LLM-driven medical consultations. Unlike existing benchmarks, 3MDBench simulates real-world patient variability by incorporating four temperament-driven Patient Agents and an Assessor Agent that evaluates diagnostic accuracy and dialogue quality. The benchmark integrates textual and image-based patient data across 34 common diagnoses, mirroring real-world telemedicine interactions. Under different diagnostic strategies, we evaluate state-of-the-art LVLMs. Our findings demonstrate that incorporating dialogue improves the F1 score from 50.4 to 54.2 compared to non-dialogue settings, underscoring the value of context-driven, information-seeking questioning. Additionally, we demonstrate that multimodal inputs enhance diagnostic efficiency. Image-supported models outperform text-only counterparts by raising the diagnostic F1 score from 52.8 to 54.2 in a similar dialogue setting. Finally, we suggest an approach that improves the diagnostic F1-score to 70.3 by training the CNN model on the diagnosis prediction task and incorporating its top-3 predictions into the LVLM context. 3MDBench provides a reproducible and extendable evaluation framework for AI-driven medical assistants. It offers insights into how patient temperament, dialogue strategies, and multimodal reasoning influence diagnosis quality. By addressing real-world complexities in telemedicine, our benchmark paves the way for more empathetic, reliable, and context-aware AI-driven healthcare solutions. The source code of our benchmark is publicly available: https://github.com/univanxx/3mdbench

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