mlsanityData to 7 Oct 2026

From Screens to Scenes: A Survey of Embodied AI in Healthcare

arXiv 2501.07468 ↗First posted 13 Jan 2025cs.AIcited by 10

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Abstract (arXiv)

Healthcare systems worldwide face persistent challenges in efficiency, accessibility, and personalization. Powered by modern AI technologies such as multimodal large language models and world models, Embodied AI (EmAI) represents a transformative frontier, offering enhanced autonomy and the ability to interact with the physical world to address these challenges. As an interdisciplinary and rapidly evolving research domain, "EmAI in healthcare" spans diverse fields such as algorithms, robotics, and biomedicine. This complexity underscores the importance of timely reviews and analyses to track advancements, address challenges, and foster cross-disciplinary collaboration. In this paper, we provide a comprehensive overview of the "brain" of EmAI for healthcare, wherein we introduce foundational AI algorithms for perception, actuation, planning, and memory, and focus on presenting the healthcare applications spanning clinical interventions, daily care & companionship, infrastructure support, and biomedical research. Despite its promise, the development of EmAI for healthcare is hindered by critical challenges such as safety concerns, gaps between simulation platforms and real-world applications, the absence of standardized benchmarks, and uneven progress across interdisciplinary domains. We discuss the technical barriers and explore ethical considerations, offering a forward-looking perspective on the future of EmAI in healthcare. A hierarchical framework of intelligent levels for EmAI systems is also introduced to guide further development. By providing systematic insights, this work aims to inspire innovation and practical applications, paving the way for a new era of intelligent, patient-centered healthcare.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 153 of its 631 references that are in the map. First posted on arXiv 13 Jan 2025.

Builds on

153 of 631 references.

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50 most cited of 153 All 153 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 3.

10 papers.

  1. 2025Multi-agent Embodied AI: Advances and Future Directionscited by 36
  2. 2025A Survey on Agentic Multimodal Large Language Modelscited by 19
  3. 2025Mixture of Experts in Large Language Modelscited by 15
  4. 2025Neural Brain: A Neuroscience-inspired Framework for Embodied Agentscited by 10
  5. 2025Safety of Embodied Navigation: A Surveycited by 9
  6. 2026Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Actioncited by 2
  7. 2025Advances in Large Language Models for Medicinecited by 1
  8. 2025A cybersecurity AI agent selection and decision support frameworkcited by 0
  9. 2026SAMe: A Semantic Anatomy Mapping Engine for Robotic Ultrasoundcited by 0
  10. 2026Listening and Mirroring: The Effects of Verbal Attunement and Behavioral Mimicry on Social and Empathic Perceptions of Embodied AI Agents in VRcited by 0

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