{"ID":2841182,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12008","arxiv_id":"2511.12008","title":"Adaptive Diagnostic Reasoning Framework for Pathology with Multimodal Large Language Models","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.","short_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 interpreta...","url_abs":"https://arxiv.org/abs/2511.12008","url_pdf":"https://arxiv.org/pdf/2511.12008v1","authors":"[\"Yunqi Hong\",\"Johnson Kao\",\"Liam Edwards\",\"Nein-Tzu Liu\",\"Chung-Yen Huang\",\"Alex Oliveira-Kowaleski\",\"Cho-Jui Hsieh\",\"Neil Y. C. Lin\"]","published":"2025-11-15T03:06:59Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CV\",\"cs.LG\"]","methods":"[\"Language Model\"]","has_code":false}