Adaptive Diagnostic Reasoning Framework for Pathology with Multimodal Large Language Models

• URL: http://arxiv.org/abs/2511.12008v1
• Date: Sat, 15 Nov 2025 03:06:59 GMT
• Title: Adaptive Diagnostic Reasoning Framework for Pathology with Multimodal Large Language Models
• Authors: Yunqi Hong, Johnson Kao, Liam Edwards, Nein-Tzu Liu, Chung-Yen Huang, Alex Oliveira-Kowaleski, Cho-Jui Hsieh, Neil Y. C. Lin,
• Abstract summary: We present RECAP-PATH, an interpretable framework that establishes a self-learning paradigm.<n>It shifts off-the-shelf multimodal large language models from passive pattern recognition to evidence-linked diagnostic reasoning.<n>This self-learning approach requires only small labeled sets and no white-box access or weight updates to generate cancer diagnoses.
• Score: 34.28963665009494
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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