arXiv:2606.28556v1 Announce Type: new 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 syntheti
The proliferation of advanced LLMs and vision-language models makes their application to complex medical reasoning a natural next step, necessitating robust evaluation benchmarks.
This benchmark directly addresses critical limitations in current medical AI evaluation, enabling more comprehensive and clinically relevant assessment of multimodal LLMs for healthcare applications.
The availability of IMCBench allows for more rigorous development and comparison of multimodal AI systems capable of handling image-grounded, multi-turn medical conversations, bridging current fragmentation.
- · AI healthcare researchers
- · Medical AI developers
- · Diagnostic imaging companies
- · Hospitals and clinics adopting AI
- · Companies relying on fragmented or single-turn medical AI evaluation methods
Improved multimodal AI models for medical diagnosis and clinical decision support.
Accelerated development of AI agents capable of nuanced, interactive medical consultations.
Enhanced patient outcomes through AI-assisted triaging and potentially reducing diagnostic errors.
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Read at arXiv cs.AI