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.
@article{arxiv.2511.12008,
title = {Adaptive Diagnostic Reasoning Framework for Pathology with Multimodal Large Language Models},
author = {Yunqi Hong and Johnson Kao and Liam Edwards and Nein-Tzu Liu and Chung-Yen Huang and Alex Oliveira-Kowaleski and Cho-Jui Hsieh and Neil Y. C. Lin},
journal= {arXiv preprint arXiv:2511.12008},
year = {2025}
}