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Survey Maps Medical Embodied AI for Next-Gen Healthcare.

Cheng Zhang, Qing Cai, Xingzheng Wu, Xun Yang, Xiaojun Chang, Bingkun Bao, Liqiang Nie, Xinwang Liu, Yi Yang· June 16, 2026 View original

Key takeaways

  • Embodied AI is crucial for integrating AI into physical clinical workflows.
  • The survey provides a system-level view of medical embodied AI, covering perception, decision, and action.
  • It highlights applications, datasets, and challenges in real-world clinical practice.
  • Future research needs to focus on safe and effective integration of embodied AI in healthcare.

Who benefits

HealthcareMedical DevicesRoboticsPharmaceuticalsResearch & Development

Summary

This survey provides a comprehensive overview of medical embodied AI, focusing on how intelligent agents integrate perception, decision-making, and action within clinical environments. It reviews representative applications, relevant datasets, and major challenges, offering a unified system-level understanding of this rapidly expanding field.

Foundation models have significantly improved efficiency across various healthcare applications. However, their limited ability to interact with the physical world restricts their utility in real-world clinical settings, where critical decisions and physical actions are closely linked. This gap is being addressed by the emerging field of embodied artificial intelligence (AI), which enables agents to operate within complex medical environments. As research in medical embodied AI rapidly expands, there's a growing need to understand how these intelligent agents function as integrated, end-to-end systems in clinical workflows. Existing surveys often focus on individual components, lacking a holistic view. This new survey aims to consolidate recent advancements by systematically reviewing the core components of medical embodied AI, emphasizing the coordinated integration of perception, decision-making, and action. The paper further reviews key medical applications and relevant datasets, while also analyzing the significant challenges encountered in practical clinical use. It concludes by discussing crucial directions for future research in this evolving domain, providing a valuable resource for researchers and practitioners.

Why it matters

Healthcare professionals, researchers, and technology developers should pay attention to this survey as it outlines the future trajectory of AI in clinical practice, moving beyond mere data analysis to physical interaction and autonomous assistance. Understanding this field is crucial for developing safe, effective, and integrated AI solutions for next-generation healthcare.

How to implement this in your domain

  1. 1Review the survey to identify promising medical embodied AI applications relevant to your clinical or research area.
  2. 2Explore existing datasets and benchmarks for medical embodied AI to inform new research or development projects.
  3. 3Collaborate with AI researchers to design and test embodied AI systems that integrate perception, decision-making, and action in simulated clinical environments.
  4. 4Assess the ethical and safety implications of deploying embodied AI in healthcare, establishing guidelines for responsible implementation.

Original post by Cheng Zhang, Qing Cai, Xingzheng Wu, Xun Yang, Xiaojun Chang, Bingkun Bao, Liqiang Nie, Xinwang Liu, Yi Yang

"arXiv:2606.15647v1 Announce Type: new 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 w…"

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Primary sources

Originally posted by Cheng Zhang, Qing Cai, Xingzheng Wu, Xun Yang, Xiaojun Chang, Bingkun Bao, Liqiang Nie, Xinwang Liu, Yi Yang on X · view source

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