Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action
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 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 op
The increasing performance of foundation models in AI is now driving their application into more complex, physically interactive domains like healthcare, necessitating new paradigms.
This development signals a critical step towards AI systems that can not only process data but also perceive, reason, and act in real-world clinical environments, significantly enhancing healthcare capabilities.
The focus shifts from purely data-driven AI in healthcare to embodied AI systems capable of physical interaction, enabling more direct and effective intervention in medical workflows.
- · Healthcare AI Developers
- · Medical Robotics Companies
- · Patients (long-term)
- · Hospitals and Clinics
- · Traditional Medical Device Manufacturers (if slow to adapt)
- · Healthcare Providers reliant solely on manual processes
Embodied AI will begin to integrate into surgical assistance, patient care, and diagnostic processes.
This integration will lead to improved safety, efficiency, and accessibility of advanced medical procedures.
The widespread adoption of embodied AI could fundamentally alter the roles of human clinicians, shifting focus towards oversight and complex decision-making rather than routine physical tasks.
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Read at arXiv cs.AI