Adaptive Diagnostic Reasoning Framework for Pathology with Multimodal Large Language Models

TL;DR

RECAP-PATH is an interpretable, self-learning framework that enhances multimodal large language models for pathology diagnosis, providing human-readable reasoning and improved accuracy with minimal labeled data.

Contribution

It introduces a novel two-phase self-learning paradigm that links evidence to diagnosis, enabling off-the-shelf models to produce interpretable and accurate pathology diagnoses without extensive retraining.

Findings

Achieved alignment of rationales with expert assessments

Significant improvements in diagnostic accuracy over baseline models

Operates effectively with small labeled datasets

Abstract

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.