IMCBench: A benchmark for multimodal LLMs in Image-grounded Medical Conversations
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,...
| Authors | Maria Xenochristou, Ashutosh Joshi, Korosh Vatanparvar et al. |
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| Primary category | cs.AI |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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