Kai Yu, Shuang Zhou, Yu Hou, Yiran Song, Min Zeng, Fang Tian, Jin Du, Wenya Xie, Biao Yin, You Chen, Feifan Liu, Jie Ding, Zirui Liu, Mingquan Lin, Rui Zhang
Abstract Multimodal artificial intelligence (AI) agents are emerging in healthcare as systems that integrate heterogeneous clinical data, foundation models (FMs), tools, and agentic workflows, but their applications and translational readiness remain unclear. We conducted a scoping review of 37 peer-reviewed studies published between January 2022 and June 2025. Included studies covered clinical decision support ( N = 17), clinical documentation and report generation ( N = 3), clinical monitoring and health management ( N = 13), and medical education and training ( N = 4). We synthesized modality combinations and fusion strategies, FM utilization and agent architectures, tool integration, agent capabilities, and evaluation practices. Current systems were predominantly text-centric, frequently used closed-source FMs, and remained concentrated in prototype or early technical evaluation stages. Safety, fairness, prospective outcome-based validation, and real-world deployment evidence were limited. These findings suggest that multimodal AI agents are best interpreted as emerging augmentative systems requiring stronger evaluation before clinical translation.