Multimodal Artificial Intelligence Agents in Healthcare: A Scoping Review

Multimodal Artificial Intelligence Agents in Healthcare: A Scoping Review | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 October 2025 V1 Latest version Share on Multimodal Artificial Intelligence Agents in Healthcare: A Scoping Review Authors : Kai Yu , Shuang Zhou 0000-0001-5739-1637 [email protected] , Yu Hou , Yiran Song , Min Zeng , Fangqiao Tian , Jin Du , … Show All … , Wenya Xie , Biao Yin , You Chen , Feifan Liu , Jie Ding , Zirui Liu , Mingquan Lin , and Rui Zhang Show Fewer Authors Info & Affiliations https://doi.org/10.22541/au.176055853.39564234/v1 1010 views 386 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Intelligent agent systems are gaining attention in healthcare, yet unimodal designs constrain their ability to process the heterogeneous multimodal data required for complex clinical tasks. Multimodal artificial intelligence (AI) agent systems have recently emerged as a promising paradigm that integrates diverse data, leverages large foundation models (FMs), and coordinates multiple agents and tools. However, their applications, challenges, and future directions remain to be systematically synthesized. This review addresses this gap through a scoping review of recent 37 studies spanning four major clinical applications: clinical decision support, clinical documentation and report generation, clinical monitoring and health management, and medical education and training. We analyze modality distributions and fusion strategies, FM utilization and agent architectures, tool integration, and key agent capabilities. Evaluation practices are examined across multiple dimensions including effectiveness, efficiency, robustness, fairness, explainability, safety, and usability. Despite notable progress, current systems continue to face critical limitations. We outline future research directions to advance further development, evaluation, and clinical translation of multimodal AI agent systems in healthcare. Supplementary Material File (multi-modal agent review.pdf) Download 3.78 MB Information & Authors Information Version history V1 Version 1 15 October 2025 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License Keywords biomedical agents computing and processing large language models multi-modal Authors Affiliations Kai Yu Division of Computational Health Sciences, Department of Surgery, University of Minnesota View all articles by this author Shuang Zhou 0000-0001-5739-1637 [email protected] Division of Computational Health Sciences, Department of Surgery, University of Minnesota View all articles by this author Yu Hou Division of Computational Health Sciences, Department of Surgery, University of Minnesota View all articles by this author Yiran Song Division of Computational Health Sciences, Department of Surgery, University of Minnesota View all articles by this author Min Zeng Division of Computational Health Sciences, Department of Surgery, University of Minnesota View all articles by this author Fangqiao Tian School of Statistics, University of Minnesota View all articles by this author Jin Du School of Statistics, University of Minnesota View all articles by this author Wenya Xie Department of Computer Science & Engineering, University of Minnesota View all articles by this author Biao Yin Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School View all articles by this author You Chen Department of Biomedical Informatics, Vanderbilt University Medical Center View all articles by this author Feifan Liu Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School View all articles by this author Jie Ding School of Statistics, University of Minnesota View all articles by this author Zirui Liu Department of Computer Science & Engineering, University of Minnesota View all articles by this author Mingquan Lin Division of Computational Health Sciences, Department of Surgery, University of Minnesota View all articles by this author Rui Zhang Division of Computational Health Sciences, Department of Surgery, University of Minnesota View all articles by this author Metrics & Citations Metrics Article Usage 1010 views 386 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Kai Yu, Shuang Zhou, Yu Hou, et al. Multimodal Artificial Intelligence Agents in Healthcare: A Scoping Review. Authorea . 15 October 2025. DOI: https://doi.org/10.22541/au.176055853.39564234/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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