Advances in Interpretable Diagnostic Reasoning for Pathology and Dermatology

The field of medical diagnosis is moving towards more interpretable and trustworthy AI systems. Recent developments have focused on creating frameworks that provide human-readable reasoning and evidence-linked diagnostic reasoning, shifting from passive pattern recognition to active decision-making. This shift is driven by the need for clinically trustworthy AI systems that can provide accurate diagnoses and inform treatment decisions.

Notable papers in this area include:

  • A paper presenting RECAP-PATH, a framework that establishes a self-learning paradigm for diagnostic reasoning in pathology, which produced rationales aligned with expert assessment and delivered substantial gains in diagnostic accuracy.
  • A paper proposing SkinR1, a novel dermatological vision-language model that combines deep textbook-based reasoning with reinforcement learning, achieving superior diagnostic accuracy on multiple dermatology datasets.
  • A paper introducing SkinGPT-R1, a dermatology-focused vision language model that makes diagnostic chain of thought reasoning explicit and verifiable, achieving an average score of 4.031 out of 5 on the DermBench benchmark.

Sources

Adaptive Diagnostic Reasoning Framework for Pathology with Multimodal Large Language Models

Explaining Digital Pathology Models via Clustering Activations

Skin-R1: Toward Trustworthy Clinical Reasoning for Dermatological Diagnosis

SkinGPT-R1: Adapter-Only Dual Distillation for Efficient Dermatology Reasoning

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