arXiv:2511.14900v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis. However, their trustworthiness and clinical utility remain limited by three key challenges: heterogeneous datasets with inconsistent diagnostic labels and concept annotations, the lack of grounded diagnostic rationales for reliable reasoning supervision, and limited scalability when transferring knowledge from small, densely annotated datasets to large collections with sparse labels. To address these challenges, we pr
The proliferation of advanced vision-language models (VLMs) and increasing investment in AI for healthcare creates a timely convergence for addressing specific challenges in medical diagnosis.
This development represents a significant step towards more reliable and scalable AI assistance in clinical settings, potentially improving diagnostic accuracy and access in specialized medical fields.
The explicit focus on guided diagnostic rationales and overcoming data inconsistencies shifts AI from simple pattern recognition to more trustworthy and explainable clinical decision support.
- · AI healthcare startups
- · Dermatologists
- · Patients in underserved areas
- · Medical technology sector
- · Traditional diagnostic pathways reliant solely on human expertise
- · Medical AI models lacking explainability
Improved diagnostic accuracy and efficiency in dermatology through VLM assistance.
Accelerated development and adoption of AI tools across other medical specialties facing similar diagnostic challenges.
Potential for AI to democratize access to specialized medical diagnosis globally, reducing healthcare disparities.
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Read at arXiv cs.AI