Integrating general-purpose multimodal foundation models into medical applications

Determine effective methodologies to integrate general-purpose multimodal foundation models into medical applications and optimize these models for domain-specific semantics and diagnostic reasoning in clinical settings.

Background

The paper surveys recent progress in large multimodal foundation models such as Qwen2.5-VL, Qwen3-VL, and InternVL-3, noting their strong capabilities on general vision–language tasks. It also highlights growing interest in domain-specific medical multimodal models (e.g., Lingshu-32B) designed for medical image interpretation.

Despite this progress, the authors emphasize that translating general-purpose multimodal models to clinical domains remains challenging. Medical tasks require alignment with domain-specific semantics, safety, and diagnostic reasoning. The paper’s proposed SkinFlow focuses on dermatology, but it identifies the broader, field-level challenge of devising effective strategies to integrate and adapt general multimodal foundation models for medical use cases that demand clinically grounded understanding and decision support.

References

Nevertheless, how to effectively integrate general-purpose multimodal foundation models into medical applications—and optimize them for domain-specific semantics and diagnostic reasoning—remains an open and underexplored problem.

— SkinFlow: Efficient Information Transmission for Open Dermatological Diagnosis via Dynamic Visual Encoding and Staged RL  (2601.09136 - Liu et al., 14 Jan 2026) in Section 2.4 (Emergence of Next-Generation Multimodal Foundation Models), Related Work

Therefore, developing multimodal AI systems that align imaging and physician-authored narratives with clinically meaningful prediction objectives, while enabling reliable and interpretable reasoning, remains a critical unresolved challenge.

— From Image Interpretation to Clinical Reasoning: Upstream Physician-Context-Aware Multimodal Learning with Causal Reinforcement Learning  (2609.38924 - Pi et al., 30 Sep 2026) in Section 2, Background, second paragraph