通用视觉语言模型（VLMs）能否在医疗领域超越专门化模型？基准测试与战略洞察
Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insights
AI总结：
研究比较了通用与专门化医疗VLMs的性能，发现高效微调的通用模型在多数任务中表现可比或更优，尤其在处理未见医疗模态时。
摘要：
视觉语言模型（VLMs）在临床环境中展示了自动化图像诊断和解释的潜力。然而，开发专门化的医疗VLMs需要大量计算资源和精心编纂的数据集，且不清楚在何种条件下通用和专门化医疗VLMs各自表现最佳。本研究强调了专门化医疗VLMs和通用VLMs的互补优势。专门化模型在模态对齐的使用场景中仍具价值，但研究发现高效微调的通用VLMs在大多数任务中可达到可比或甚至更优的性能，尤其是在转移到未见或罕见的OOD医疗模态时。这些结果表明，通用VLMs，而非受限于缺乏专门化医疗预训练，可能提供了一条可扩展且成本效益高的临床AI发展途径。
英文摘要：
Vision Language Models (VLMs) have shown promise in automating image diagnosis and interpretation in clinical settings. However, developing specialist medical VLMs requires substantial computational resources and carefully curated datasets, and it remains unclear under which conditions generalist and specialist medical VLMs each perform best. This study highlights the complementary strengths of specialist medical and generalist VLMs. Specialists remain valuable in modality-aligned use cases, but we find that efficiently fine-tuned generalist VLMs can achieve comparable or even superior performance in most tasks, particularly when transferring to unseen or rare OOD medical modalities. These results suggest that generalist VLMs, rather than being constrained by their lack of specialist medical pretraining, may offer a scalable and cost-effective pathway for advancing clinical AI development.
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