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Limited patient understanding of medical decisions may reduce adherence and lead to doctor-patient conflicts. Existing dialogue systems either lack interpretability or suffer from rigid architectures and limited scalability. This study aims to develop an interpretable, patient-centered medical dialogue system to improve communication and decision support.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose an intention-oriented multi-agent dialogue system consisting of an intention understanding agent, an argument reasoning agent, and a template-based interaction agent. A medical knowledge ontology was constructed using a hybrid expert-data-driven approach, and a knowledge base was built from recent biomedical literature. The system performs intent recognition, entity extraction, and argument-based reasoning using weighted clinical evidence. A Graves\u2019 eye disease dialogue platform was implemented to evaluate system performance through objective metrics and clinician-based user studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The system achieved an average F1 score of 85% for intent recognition and over 95% response accuracy in evaluations by physicians. User experience assessments showed performance comparable to physician-led online systems and superior to traditional medical Q&amp;A systems. In comparative experiments, the proposed system significantly outperformed ChatGPT in personalized reasoning and interpretability across multiple evaluation dimensions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed multi-agent dialogue system improves patient-centered communication by providing interpretable and personalized decision support. It demonstrates strong potential to enhance healthcare dialogue systems and assist clinical decision-making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-026-03498-x","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T09:06:22Z","timestamp":1779786382000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An intention-oriented multi-agent dialogue system for patient-centered decision-making"],"prefix":"10.1186","volume":"26","author":[{"given":"Pengfei","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,26]]},"reference":[{"issue":"9","key":"3498_CR1","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1093\/jamia\/ocy072","volume":"25","author":"L Laranjo","year":"2018","unstructured":"Laranjo L, Dunn AG, Tong HL, Kocaballi AB, Chen JA, Bashir R, et al. 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