Adaptive Diagnostic Reasoning Framework for Pathology with Multimodal Large Language Models
This work addresses the need for interpretable and trustworthy AI in pathology to improve clinical adoption and prevent errors, representing a novel method for a known bottleneck.
The authors tackled the problem of limited adoption of AI tools in pathology due to lack of human-readable reasoning by developing RECAP-PATH, a framework that shifts multimodal large language models to evidence-linked diagnostic reasoning, resulting in substantial gains in diagnostic accuracy on breast and prostate datasets.
AI tools in pathology have improved screening throughput, standardized quantification, and revealed prognostic patterns that inform treatment. However, adoption remains limited because most systems still lack the human-readable reasoning needed to audit decisions and prevent errors. We present RECAP-PATH, an interpretable framework that establishes a self-learning paradigm, shifting off-the-shelf multimodal large language models from passive pattern recognition to evidence-linked diagnostic reasoning. At its core is a two-phase learning process that autonomously derives diagnostic criteria: diversification expands pathology-style explanations, while optimization refines them for accuracy. This self-learning approach requires only small labeled sets and no white-box access or weight updates to generate cancer diagnoses. Evaluated on breast and prostate datasets, RECAP-PATH produced rationales aligned with expert assessment and delivered substantial gains in diagnostic accuracy over baselines. By uniting visual understanding with reasoning, RECAP-PATH provides clinically trustworthy AI and demonstrates a generalizable path toward evidence-linked interpretation.