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[2506.17337] Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insights
Summary
This study evaluates the performance of generalist Vision Language Models (VLMs) compared to specialist medical VLMs, revealing that generalist models can achieve comparable or superior results in various medical tasks.
Why It Matters
As healthcare increasingly integrates AI for diagnostics, understanding the effectiveness of generalist versus specialist models is crucial. This research highlights the potential for generalist VLMs to provide a cost-effective and scalable solution in clinical settings, which could enhance AI adoption in healthcare.
Key Takeaways
- Generalist VLMs can match or exceed the performance of specialist medical VLMs in many tasks.
- Efficient fine-tuning of generalist models enhances their applicability to unseen medical modalities.
- Specialist models remain valuable for modality-aligned use cases, but generalists offer scalability.
- The findings suggest a shift towards using generalist models in clinical AI development.
- Cost-effectiveness of generalist VLMs may accelerate AI integration in healthcare.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2506.17337 (eess) [Submitted on 19 Jun 2025 (v1), last revised 21 Feb 2026 (this version, v3)] Title:Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insights Authors:Yuan Zhong, Ruinan Jin, Qi Dou, Xiaoxiao Li View a PDF of the paper titled Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insights, by Yuan Zhong and 3 other authors View PDF HTML (experimental) Abstract: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. Comments: Subjects: Image and Video...
