Foundation models have shown impressive performance in enhancing healthcare efficiency across a wide range of medical applications, according to a new survey paper published on arXiv. However, the authors note that the models' 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.
To address this, the paper presents a systematic survey of medical embodied artificial intelligence (AI), an emerging paradigm that enables intelligent agents to operate in complex medical environments. The survey emphasizes the coordinated integration of three core components: perception, decision-making, and action.
The Three Pillars of Medical Embodied AI
The survey organizes the field around perception, decision-making, and action as an end-to-end system. According to the authors, existing surveys on medical embodied AI largely emphasize individual aspects or functional components, lacking a unified system-level organization of the field. This work aims to fill that gap by consolidating recent advances and focusing on how intelligent agents function as integrated systems in clinical environments.
"Embodied artificial intelligence (AI) has emerged as a promising physical-interactive paradigm for intelligent healthcare, enabling agents to operate in complex medical environments."
Applications and Datasets
The paper reviews representative medical applications and relevant datasets that support the development of embodied AI. While specific applications are not detailed in the abstract, the authors indicate that they cover a broad range of use cases where perception, decision-making, and action must work together. The survey also analyzes the major challenges encountered in real-world clinical practice, which include issues related to safety, reliability, and integration into existing workflows.
Challenges and Future Directions
Key challenges identified include the need for robust perception in dynamic medical settings, safe decision-making under uncertainty, and precise physical action execution. The authors discuss key directions for future research in this rapidly evolving field, though specific directions are not enumerated in the abstract. The paper is accompanied by a project page at the provided URL, offering additional resources.
Implications for Enterprise Technology Leaders
For CTOs and technology decision-makers in healthcare, this survey provides a structured framework for understanding how embodied AI can move beyond isolated AI components toward integrated systems that interact physically with clinical environments. The emphasis on system-level integration highlights the need for coordinating multiple AI capabilities — a challenge that parallels similar integration efforts in supply chain and logistics automation, where perception (sensors, IoT), decision-making (optimization algorithms), and action (robotic execution) must also be tightly coupled. While the paper focuses on healthcare, the architectural lessons apply broadly to any domain requiring physical-world interaction.
| Component | Description |
|---|---|
| Perception | Sensing and interpreting the physical medical environment |
| Decision-Making | Making safety-critical choices based on perceived data |
| Action | Executing physical tasks in clinical workflows |
The survey is authored by Zhang, Cheng, Cai, Qing, Wu, Xingzheng, Yang, Xun, Chang, Xiaojun, Bao, Bingkun, Nie, Liqiang, Liu, Xinwang, and Yi, and is available at https://arxiv.org/abs/2606.15647. As research in medical embodied AI rapidly expands, this survey serves as a foundational reference for both researchers and practitioners aiming to build next-generation intelligent healthcare systems.
