IMCBench: A benchmark for multimodal LLMs in Image-grounded Medical Conversations
IMCBench is a new benchmark for evaluating multimodal LLMs in image-grounded medical conversations. It combines real clinical images with synthetic patient profiles to simulate multi-turn doctor-patient interactions, assessing safety, accuracy, and appropriate use of uncertainty. Results show Claude Opus 4.6 leads with 3.61/5, but all models degrade in safety for malignant or rare conditions, and both visual input and EHR context are crucial for safe guidance.
[2606.28556] IMCBench: A benchmark for multimodal LLMs in Image-grounded Medical Conversations
[Submitted on 26 Jun 2026]
Title:IMCBench: A benchmark for multimodal LLMs in Image-grounded Medical Conversations
View a PDF of the paper titled IMCBench: A benchmark for multimodal LLMs in Image-grounded Medical Conversations, by Maria Xenochristou and 12 other authors
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Abstract:Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical applications such as decision support and triaging. However, existing medical AI benchmarks are fragmented: some support multi-turn dialogues but lack images, while others provide multimodal inputs but focus on single-turn QA tasks. To address this gap, we introduce IMCBench, an image-grounded, multi-turn medical conversation benchmark that pairs real, publicly available clinical images with synthetic patient profiles to simulate realistic patient-clinician interactions. Each conversation is evaluated across three clinical dimensions: safety, accuracy, and appropriate use of uncertainty in diagnosis. We benchmark eight multimodal frontier models across four model families (Claude, GPT, Nova, and Llama), scoring each on a 1-5 scale using LLM-as-Jury scoring calibrated against expert clinician annotations. Our results show that Claude Opus 4.6 achieves the highest overall score (3.61), followed by Claude Sonnet 4.6 (3.30) and GPT-5.2 (3.29), though no model dominates all dimensions and safety degrades for both malignant and rare conditions ($\Delta$ = -0.27 each). Ablation studies further reveal that both visual input and EHR context contribute to safe guidance (safety drops of 0.18 and 0.23 on average when each is removed), with stronger models leveraging visual features more effectively. Together, these findings demonstrate that accurate clinical description does not guarantee safe patient guidance, motivating the need for multi-dimensional evaluation frameworks in medical AI.
Comments: Accepted at ECML PKDD 2026. 22 pages, 2 figures
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.28556 [cs.AI]
(or arXiv:2606.28556v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.28556
arXiv-issued DOI via DataCite (pending registration)
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From: Maria Xenochristou [view email] [v1] Fri, 26 Jun 2026 19:18:16 UTC (866 KB)
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