CXOtoday News Desk10 minutes ago

The convergence of artificial intelligence and physical robotics is driving a profound shift in modern healthcare, moving far beyond the surgical suite to enhance patient rehabilitation, hospital logistics, remote monitoring, and long-term care. By combining continuous data, machine learning, and physical adaptability, these intelligent systems are designed not to replace human clinical judgment, but to reduce the mechanical and administrative burdens on medical teams. The ultimate goal remains firmly centered on purpose-driven innovation—using collaborative intelligence to create scalable efficiencies while ensuring that technology enhances, rather than diminishes, the patient-provider relationship.

Preparing for this evolving ecosystem requires a balanced approach that pairs advanced technological capabilities with rigorous clinical governance, data literacy, and interdisciplinary collaboration. As medical education adapts to train future physicians to critically evaluate, question, and responsibly lead AI-driven care, the focus shifts from simply deploying new tools to ensuring they serve a clinically meaningful purpose across diverse patient populations. Highlighting how smart systems enable more personalized, safe, and responsive care while preserving the vital human element of medicine is Dr. Reza Sadeghian, AI Strategist, Clinical Advisor, and Clinical Professor of Pediatrics at St. George’s University School of Medicine, Grenada.

CXOToday: How is the convergence of artificial intelligence and robotics reshaping modern healthcare beyond robotic-assisted surgery?

Dr. Reza: The convergence is much broader than the operating room. Robotics provides the physical capability to interact with the environment, while AI adds perception, prediction, learning, and greater adaptability. Together, they allow machines to do more than repeat a programmed movement.

We are already seeing applications in rehabilitation, where robotic systems can measure movement and adjust therapy to a patient’s progress; in hospitals, where autonomous devices can transport medications, supplies, and laboratory specimens; and in pharmacies and laboratories, where robotics can improve the consistency of repetitive processes. We are also seeing growing potential in remote monitoring, prosthetics, elder care, disinfection, and assistance for patients with mobility limitations.

The most meaningful opportunity is not simply replacing a manual task. It is connecting robotic capabilities with clinical data so that support can become more personalized and responsive. A rehabilitation robot, for example, may eventually use a patient’s strength, range of motion, fatigue, and prior performance to continuously adjust the session.

That said, the technology should remain purpose-driven. We should begin with the clinical or operational problem, not with the robot, and then determine whether robotics, AI, or a combination of both is the appropriate solution.

Key takeaway: AI gives robotics greater intelligence, but healthcare must give it a clinically meaningful purpose.

CXOToday: Which areas of healthcare are benefiting most from AI-powered robotics today, and what advancements do you expect?

Dr. Reza:Today, the strongest use cases are generally in structured environments where the tasks are measurable and the risks can be carefully controlled.

Rehabilitation is one of the most promising areas. Robotic devices can support repetitive movement, measure patient performance, and allow therapists to deliver more individualized training. Assistive robotics is also expanding in elder care, mobility support, and activities of daily living, although the evidence is still developing and usability remains a challenge. Systematic reviews suggest that socially assistive robots can improve engagement with physical activity among some older adults, but they should supplement, not replace, human caregivers.

Hospital logistics is another practical opportunity. Robots can transport supplies or medications, support environmental services, and reduce the time clinical staff spend on nonclinical tasks. Smart prosthetics and exoskeletons are also advancing as AI enables devices to respond more naturally to movement and individual patient needs.

Over the next decade, I expect robotics to become more adaptive, mobile, and connected. We will likely see greater use in home-based rehabilitation, remote examination, precision procedures, medication management, and support for aging populations. The major shift will be from robots that follow fixed instructions to systems that can respond to clinical context within clearly defined safety boundaries.

Key takeaway: The near-term winners will be repetitive, measurable tasks where robotics expands human capacity without removing human oversight.

CXOToday: How can AI complement rather than replace doctors’ expertise and decision-making?

Dr. Reza:The best model is collaborative intelligence. AI and robotics should perform the tasks they are well suited for, continuous monitoring, pattern recognition, measurement, information retrieval, repetitive movement, and administrative work, while physicians retain responsibility for interpretation, judgment, communication, and the final clinical decision.

Medicine is not simply matching data to an answer. A physician considers uncertainty, patient preferences, family circumstances, ethical concerns, comorbidities, and subtle changes that may not appear in a dataset. AI may identify a pattern or recommend an action, but the physician must determine whether that recommendation makes sense for the person in front of them.

We therefore need to design systems with meaningful human control. Clinicians should understand what the system is doing, when it is uncertain, what data contributed to its output, and how to override it. The level of human validation should also correspond to the level of risk. A robot delivering linens is very different from a system influencing diagnosis or treatment.

The World Health Organization has consistently emphasized human autonomy, transparency, accountability, safety, and equity as central principles for responsible healthcare AI.

My philosophy is simple: cool technology should enable warm hands. AI should reduce the mechanical burden of medicine so clinicians can devote more attention to listening, reasoning, and caring.

CXOToday: What skills should future doctors develop to succeed in an AI-enabled healthcare ecosystem?

Dr. Reza:Future physicians do not all need to become programmers, but they do need to become informed users and responsible stewards of technology. First, they need foundational AI literacy. They should understand concepts such as training data, bias, hallucination, sensitivity, specificity, model drift, and the difference between a prediction and a clinical conclusion.

Second, they need strong critical-appraisal skills. A polished AI answer can still be wrong. Physicians must be able to question the output, compare it with the clinical evidence, and recognize when a system is operating outside its intended context.

Third, future doctors need data and workflow literacy. They should understand how information enters the EHR, how algorithms fit into clinical workflows, and how poor implementation can create new risks or cognitive burden.

Communication, ethics, leadership, and interdisciplinary teamwork will also become even more important. Physicians will increasingly collaborate with engineers, data scientists, informaticians, compliance professionals, and operational leaders. Most importantly, we should not allow technology to weaken traditional clinical skills. History-taking, physical examination, empathy, and clinical reasoning become more, not less, important when AI is present. The physician of the future must know both how to use technology and when not to rely on it.

Key takeaway: The critical skill is not merely using AI; it is knowing how to question, validate, and govern it.

CXOToday: How are medical schools like St. George’s University preparing students through simulation, global clinical exposure, and emerging technologies?

Dr. Reza: Medical schools such as St. George’s University are increasingly preparing students by combining foundational medical education with simulation, early clinical-skills development, and broad exposure to different healthcare environments.

At SGU, simulation is used to help students strengthen clinical reasoning and decision-making while practicing communication, teamwork, physical examination, and procedural skills in a controlled environment. This gives students an opportunity to make decisions, receive feedback, and improve before applying those skills in direct patient care. SGU’s Simulation Program received full accreditation through December 31, 2030, including accreditation in teaching and education and assessment.

The curriculum also includes practical clinical-skills training. For example, students practice hypothesis-driven approaches to patient presentations, comprehensive physical examinations, SOAP-note documentation, communication, and constructive feedback.

Another important strength is SGU’s international clinical-training network. The university reports affiliations with more than 85 hospitals and health systems across the United States, Canada, and the United Kingdom. This gives students exposure to different patient populations, clinical settings, healthcare-delivery models, and cultural perspectives.

With emerging technologies, I would distinguish between what is already established and what is still developing. SGU faculty and researchers are examining areas such as artificial intelligence in medicine and medical education, but the broader opportunity is to integrate AI literacy more deliberately into simulation and clinical training. Students should learn not only how to use emerging technologies, but also how to question their outputs, recognize bias and hallucinations, protect patient privacy, and understand when human judgment must override the technology.

Simulation offers an ideal setting for that next step. Students could be presented with an AI-generated recommendation, asked to assess whether it is clinically sound, identify missing or inaccurate information, and then explain the final decision to the patient.

The goal is not simply to expose students to more technology. It is to prepare physicians who can use technology thoughtfully, safely, and humanely across different healthcare systems.

Key takeaway: We should train students with technology before expecting them to practice safely alongside it.

CXOToday: What role will interdisciplinary collaboration play in the next wave of medical innovation?

It will be essential. Healthcare innovation fails when technical teams build in isolation from the people who deliver and receive care. Clinicians understand the disease, patient relationship, and workflow. Engineers understand hardware, sensors, control systems, and reliability. AI specialists understand data, model performance, and uncertainty.

Human-factors experts understand usability. Compliance, privacy, cybersecurity, ethics, and operational leaders understand the broader risks and requirements. No single group has the complete picture. The physician’s role should not be limited to reviewing a finished product at the end. Clinicians and patients should help define the problem from the beginning, establish the meaningful outcome, identify foreseeable harms, and participate in real-world validation. This is why I favor a trusted-advisors model. Clinical departments define their highest-priority problems, and technical and operational teams work with them to evaluate potential solutions. That creates local ownership and results in technologies that fit the workflow rather than forcing clinicians to adapt around the technology.

Interdisciplinary collaboration is also how we avoid solving the wrong problem. A technically impressive robot that adds steps, creates maintenance burdens, or disrupts patient communication may not represent meaningful innovation.

Key takeaway: The next breakthrough will not come from AI alone, robotics alone, or medicine alone. It will come from those disciplines working together around a real patient-care problem.

CXOToday: What opportunities and challenges do you foresee over the next decade?

Dr. Reza: The opportunities are substantial. AI-powered robotics could expand rehabilitation, support independent living, address workforce shortages, improve access in rural or underserved areas, assist clinicians with physically demanding tasks, and enable more care to occur in the home. It may also improve precision and consistency in procedures while generating objective data that helps clinicians personalize care.

Medical education could benefit as well. AI-enabled simulators could create dynamic patient scenarios, respond differently to student decisions, and provide individualized feedback. Students could practice rare emergencies or ethical dilemmas repeatedly without putting patients at risk. However, the challenges are equally significant. These systems combine software risk with physical-world risk. An inaccurate recommendation is concerning; an inaccurate recommendation connected to a machine that moves or acts can be even more consequential.

We will need strong evidence of clinical effectiveness, cybersecurity, interoperability, clear accountability, ongoing monitoring, and equitable performance across different populations and environments. Cost and infrastructure may create a divide between well-resourced systems and communities that could benefit most. Patient acceptance, privacy, workforce training, maintenance, and liability must also be addressed.

The FDA maintains oversight of AI-enabled medical devices and has emphasized lifecycle-based good machine-learning practices because these technologies can evolve and perform differently in real-world settings. My concern is not that adoption will happen too slowly. It is that excitement may sometimes move faster than evidence and governance. The organizations that succeed will not necessarily be the first to buy a robot. They will be the ones that select the right use case, involve clinicians and patients, establish clear measures, validate performance, and scale only when the technology demonstrates genuine value.

Closing thought: The future of medicine should not be more robotic. It should be more human, with robotics handling appropriate burdens so people can provide better care.