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To address these limitations, we propose MultiMedRes, a multimodal medical collaborative reasoning framework that simulates human physicians\u2019 communication by incorporating a learner agent to proactively acquire information from domain\u2010specific expert models. MultiMedRes addresses medical multimodal reasoning problems through three steps i) Inquire: The learner agent decomposes complex medical reasoning problems into multiple domain\u2010specific sub\u2010problems; ii) Interact: The agent engages in iterative \u201cask\u2010answer\u201d interactions with expert models to obtain domain\u2010specific knowledge; and iii) Integrate: The agent integrates all the acquired domain\u2010specific knowledge to address the medical reasoning problems (e.g., identifying the difference of disease levels and abnormality sizes between medical images). We validate the effectiveness of our method on the task of difference visual question answering for X\u2010ray images. The experiments show that our zero\u2010shot prediction achieves state\u2010of\u2010the\u2010art performance, surpassing fully supervised methods, which demonstrates that MultiMedRes could offer trustworthy and interpretable assistance to physicians in monitoring the treatment progression of patients, paving the way for effective human\u2013AI interaction and collaboration.<\/jats:p>","DOI":"10.1002\/aisy.202400840","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T08:58:16Z","timestamp":1738745896000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Proactive Agent Collaborative Framework for Zero\u2010Shot Multimodal Medical Reasoning"],"prefix":"10.1002","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0168-8658","authenticated-orcid":false,"given":"Zishan","family":"Gu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering The Ohio State University  Columbus OH USA"},{"name":"Department of Biomedical Informatics The Ohio State University  Columbus OH USA"}]},{"given":"Fenglin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, Institute of Biomedical Engineering University of Oxford  Oxford UK"}]},{"given":"Jiayuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering The Ohio State University  Columbus OH USA"},{"name":"Department of Biomedical Informatics The Ohio State University  Columbus OH USA"}]},{"given":"Changchang","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering The Ohio State University  Columbus OH USA"},{"name":"Department of Biomedical Informatics The Ohio State University  Columbus OH USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4601-0779","authenticated-orcid":false,"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering The Ohio State University  Columbus OH USA"},{"name":"Department of Biomedical Informatics The Ohio State University  Columbus OH USA"},{"name":"Translational Data Analytics Institute The Ohio State University  Columbus OH USA"}]}],"member":"311","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"X.Wang Y.Peng L.Lu Z.Lu M.Bagheri R.Summers in2017 IEEE Conf. 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