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arXiv · 2606.15647

Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action

Cheng Zhang·Qing Cai·Xingzheng Wu·Xun Yang·Xiaojun Chang·Bingkun Bao·Liqiang Nie·Xinwang Liu·Yi Yang
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Abstract

Foundation models have demonstrated impressive performance in enhancing healthcare efficiency across a wide range of medical applications. Nevertheless, their limited ability to perceive, understand, and interact with the physical world significantly constrains their effectiveness in real-world clinical workflows, where safety-critical decision-making and physical execution are tightly coupled. Recently, embodied artificial intelligence (AI) has emerged as a promising physical-interactive paradigm for intelligent healthcare, enabling agents to operate in complex medical environments. As research in this area rapidly expands, understanding how intelligent agents function as integrated, end-to-end systems in clinical environments becomes increasingly critical. However, existing surveys on medical embodied AI largely emphasize individual aspects or functional components, lacking a unified system-level organization of the field. To support and consolidate recent advances, we systematically survey the core components of medical embodied AI, with a particular emphasis on the coordinated integration of perception, decision-making, and action. We further review representative medical applications and relevant datasets, and we analyze the major challenges encountered in real-world clinical practice. Finally, we discuss key directions for future research in this rapidly evolving field. The associated project can be found at https://github.com/VMVLab/Medical_Embodied_AI_Paper_List.

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Cheng Zhang, Qing Cai, Xingzheng Wu, Xun Yang, Xiaojun Chang, Bingkun Bao, Liqiang Nie, Xinwang Liu, Yi Yang. 2026-04-05. Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action. https://arxiv.org/abs/2606.15647

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