{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T20:30:49Z","timestamp":1774557049708,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Recent advances in medical multi-modal models focus on specialized image analysis like dermatology, pathology, or radiology. However, they do not fully capture the complexity of real-world clinical diagnostics, which involve heterogeneous inputs and require ongoing contextual understanding during patient-physician interactions.\nTo bridge this gap, we introduce PulseMind, a new family of multi-modal diagnostic models that integrates a systematically curated dataset, a comprehensive evaluation benchmark, and a tailored training framework. Specifically, we first construct a diagnostic dataset, MediScope, which comprises 98,000 real-world multi-turn consultations and 601,500 medical images, spanning over 10 major clinical departments and more than 200 sub-specialties. Then, to better reflect the requirements of real-world clinical diagnosis, we develop the PulseMind Benchmark, a multi-turn diagnostic consultation benchmark with a four-dimensional evaluation protocol comprising proactiveness, accuracy, usefulness, and language quality. Finally, we design a training framework tailored for multi-modal clinical diagnostics, centered around a core component named Comparison-based Reinforcement Policy Optimization (CRPO). Compared to absolute score rewards, CRPO uses relative preference signals from multi-dimensional comparisons to provide stable and human-aligned training guidance. Extensive experiments demonstrate that PulseMind achieves competitive performance on both the diagnostic consultation benchmark and public medical benchmarks.<\/jats:p>","DOI":"10.1609\/aaai.v40i13.38106","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:02:28Z","timestamp":1773792148000},"page":"11259-11268","source":"Crossref","is-referenced-by-count":1,"title":["PulseMind: A Multi-Modal Medical Model for Real-World Clinical Diagnosis"],"prefix":"10.1609","volume":"40","author":[{"given":"Jiao","family":"Xu","sequence":"first","affiliation":[]},{"given":"Junwei","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jiangwei","family":"Lao","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yunpeng","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Congyun","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Shinan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Zhihong","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Lihe","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Wang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38106\/42068","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38106\/42068","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:02:29Z","timestamp":1773792149000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i13.38106","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}