MEDSYN benchmark shows MLLMs match experts on differential diagnosis lists but have much larger gaps to final diagnosis selection than humans, due to text overreliance and cross-modal evidence gaps.
Quilt-llava: Visual instruction tuning by extracting localized narratives from open-source histopathology videos
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PMC-VQA dataset and MedVInT model achieve better generative performance on medical VQA benchmarks by visual instruction tuning on a newly constructed large-scale dataset.
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MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models
MEDSYN benchmark shows MLLMs match experts on differential diagnosis lists but have much larger gaps to final diagnosis selection than humans, due to text overreliance and cross-modal evidence gaps.
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PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
PMC-VQA dataset and MedVInT model achieve better generative performance on medical VQA benchmarks by visual instruction tuning on a newly constructed large-scale dataset.