Toward Autonomous Clinics: Human–Robot Collaboration in Clinical Care

Abstract

Robotic systems are entering clinical care, but full autonomy remains constrained by patient variability, safety requirements, dynamic clinical environments, and the need for professional oversight. Human–robot collaboration (HRC) offers a more realistic path: clinicians retain judgment and responsibility, while robots and AI support sensing, planning, action, and decision-making within defined task boundaries. In this narrative review, we examine recent advances in clinical HRC from a systems perspective, covering system architecture, learning and control, safety mechanisms, autonomy assessment, and real-world deployment. We propose a dual-brain framework, comprising a professional brain for clinical reasoning and decision support, and a physical brain for embodied sensing, planning, and task execution. Using examples from imaging, rehabilitation, surgery, and outpatient care, we argue that clinical autonomy is developing in stages from clinician-supervised imaging and decision support to bounded robotic subtasks and workflow-level coordination, instead of through unsupervised replacement of healthcare professionals. We further discuss how robustness, trust, accountability, and human factors shape safe adoption in practice. By framing autonomy as collaborative rather than substitutive, this review provides a unifying foundation for designing and evaluating the next generation of autonomous clinical systems.

Keywords

clinical autonomy / embodied intelligence / human–robot collaboration / medical robotics / safety-critical robotics / shared control

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