A Vision-Language Foundation Model for Zero-shot Clinical Collaboration and Automated Concept Discovery in Dermatology

TL;DR

DermFM-Zero is a large dermatology vision-language foundation model that achieves state-of-the-art zero-shot diagnosis and retrieval, significantly improves clinical decision-making, and offers interpretable, bias-resistant representations for safe, effective AI-assisted dermatology.

Contribution

Introduction of DermFM-Zero, a novel zero-shot dermatology foundation model trained on multimodal data, demonstrating superior performance and interpretability without task-specific fine-tuning.

Findings

Achieves state-of-the-art zero-shot performance on 20 benchmarks.

Enables primary care doctors to nearly double diagnostic accuracy.

Outperforms dermatologists in multimodal skin cancer assessment.

Abstract

Medical foundation models have shown promise in controlled benchmarks, yet widespread deployment remains hindered by reliance on task-specific fine-tuning. Here, we introduce DermFM-Zero, a dermatology vision-language foundation model trained via masked latent modelling and contrastive learning on over 4 million multimodal data points. We evaluated DermFM-Zero across 20 benchmarks spanning zero-shot diagnosis and multimodal retrieval, achieving state-of-the-art performance without task-specific adaptation. We further evaluated its zero-shot capabilities in three multinational reader studies involving over 1,100 clinicians. In primary care settings, AI assistance enabled general practitioners to nearly double their differential diagnostic accuracy across 98 skin conditions. In specialist settings, the model significantly outperformed board-certified dermatologists in multimodal skin cancer assessment. In collaborative workflows, AI assistance enabled non-experts to surpass unassisted experts while improving management appropriateness. Finally, we show that DermFM-Zero's latent representations are interpretable: sparse autoencoders unsupervisedly disentangle clinically meaningful concepts that outperform predefined-vocabulary approaches and enable targeted suppression of artifact-induced biases, enhancing robustness without retraining. These findings demonstrate that a foundation model can provide effective, safe, and transparent zero-shot clinical decision support.