Toward Autonomous Clinics: Human-Robot Collaboration in Clinical Care

Area of Science:

• Robotics in Medicine
• Artificial Intelligence in Healthcare
• Human-Robot Interaction

Background:

• Full autonomy for robotic systems in clinical settings is limited by patient variability, safety concerns, and the need for human oversight.
• Human-robot collaboration (HRC) presents a viable alternative, enabling clinicians to maintain control while leveraging AI and robotic assistance.

Purpose of the Study:

• To review recent advancements in clinical HRC from a systems perspective.
• To propose a 'dual-brain' framework for clinical autonomy.
• To discuss the staged development of clinical autonomy and factors influencing adoption.

Main Methods:

• Narrative review of current literature on clinical HRC.
• Analysis of system architecture, learning, control, safety, and deployment.
• Proposal of a dual-brain framework (professional and physical brains).

Main Results:

• Clinical autonomy is evolving through stages, from supervised tasks to coordinated robotic subtasks, not full replacement of professionals.
• Examples from imaging, rehabilitation, surgery, and outpatient care illustrate HRC applications.
• Robustness, trust, accountability, and human factors are critical for safe adoption.

Conclusions:

• Framing clinical autonomy as collaborative, rather than substitutive, offers a unified approach.
• The dual-brain framework provides a foundation for designing and evaluating future autonomous clinical systems.
• HRC enhances clinical decision-making and task execution by combining human expertise with AI and robotic capabilities.