The integration of robots, cyber-physical systems, data-driven control, and artificial intelligence (AI) plays a key role in advancing healthcare delivery and training. Cyber-Robotic Systems (CRS) are robots with intelligent computational and control capabilities that can sense, process, and respond dynamically to the environment. With the rise of data-driven and learning-based control techniques, robotic systems can now operate effectively in environments characterized by uncertainty, variability, and user-specific requirements. This is particularly crucial in areas such as robot-assisted surgery (RAS), rehabilitation, eldercare, and remote diagnostics, where human safety, adaptability, and precision are of top priority. Therefore, CRS can support Healthcare 5.0 which emphasizes personalized care, preventive diagnostics, and patient-focused treatment.
Despite significant progress in CRS, several critical challenges limit their widespread adoption in Healthcare 5.0. Current solutions often rely on expensive, medical-grade industrial robots that are unaffordable for many small institutions and lack the adaptability needed for personalized care. Additionally, the steep learning curve and long training times required for surgeons and clinical staff to operate these systems create barriers to integration in fast-paced healthcare environments. Workforce skill gaps further add to these issues, as the healthcare sector often lacks personnel with the interdisciplinary expertise needed to operate and maintain advanced robotic systems.
The Research Topic seeks to address these challenges through novel research, review articles, and experimental findings broadly focused on intelligent control and computational methods, cost-effective robotic solutions, human-robot interaction, digital twins, and virtual reality simulations enhancing healthcare delivery and remote medical training.
We welcome original research and comprehensive reviews on topics including, but not limited to: Intelligent control architectures for cyber-robotic systems across the healthcare pathway (prevention, screening, diagnosis, treatment, and rehabilitation) - AI-driven robotic assistants for minimally invasive surgical treatments and remote surgery - Human-robot collaboration in clinical diagnostic and assistive environments - Adaptive and model-free control in healthcare robotics for patient screening and treatment - Digital twin frameworks for personalized patient treatment and surgical simulation - Robotic systems for rehabilitation (rehab), eldercare, and continuous patient monitoring (prevention and screening) - Smart sensors and vision systems for real-time screening and diagnosis in medical robotics - Cybersecurity and data privacy in AI-enabled healthcare systems - Simulation-based training using teleoperated or autonomous robots in treatment and rehab scenarios - Adaptive robotic tutors and collaborative learning agents in healthcare education for prevention and rehab - Benchmarking, usability, and validation studies of healthcare robotic systems across the clinical pathway
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Clinical Trial
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Clinical Trial
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Original Research
Perspective
Policy and Practice Reviews
Review
Systematic Review
Technology and Code
Keywords: Cyber-robotic systems for Healthcare 5.0, AI-driven adaptive control, Smart sensors, Robot-assisted surgery, Teleoperated robotics for remote healthcare and education, Cybersecurity in healthcare 7. Virtual Reality and simulation-based learning
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
