Table of Contents
For the past several years, hospitals and health systems have raced to deploy artificial intelligence in the forms of ambient documentation, clinical summarization, patient messaging, coding assistance and administrative automation. But now, a growing number of IT leaders say physical AI healthcare technology will define the industry’s next major AI transformation, moving beyond the computer monitor altogether and into three-dimensional space.
What Physical AI Healthcare Technology Actually Means
Physical AI is an emerging technology that combines artificial intelligence with robotics, sensors, computer vision and autonomous systems capable of perceiving their surroundings, making decisions and taking actions in the physical world. Rather than simply generating recommendations or documentation, these systems could eventually transport medications, assist surgeons during procedures, monitor patients, guide rehabilitation, support clinical staff and coordinate hospital logistics while continuously responding to changing conditions around them.
New Questions This Technology Raises
It could fundamentally change how hospitals think about automation, shifting AI from a passive advisor into an active participant in care delivery. Healthcare organizations will need to address not only accuracy and reliability but also physical safety, human oversight, governance, cybersecurity and liability when intelligent machines begin interacting directly with patients, clinicians and the care environment.
Moving Physical AI Healthcare Technology From Information to Action
For Davit Martirosyan, co-founder and head of engineering at Expper Technologies, the defining difference between generative AI is remarkably straightforward. “For me, physical AI begins when software is no longer only generating an answer, it is generating an action in a shared physical environment,” he said.
Why Physical Interaction Raises the Stakes
“With generative AI, an incorrect response may create confusion or require someone to correct it,” Martirosyan said. “With physical AI, the system’s timing, movement, voice, distance from a person and understanding of the environment can directly affect safety, comfort and trust.” His company’s socially assistive robot, Robin, deployed in pediatric hospitals and senior care organizations, has demonstrated that intelligence inside a healthcare environment depends as much on context as on computing power. “The robot must continuously observe the person’s reaction, adapt and sometimes recognize that the best action is to pause or allow a human to take over,” he said.
A Closed Loop of Perception, Planning, and Action Within Physical AI Healthcare Technology
Dr. Filippo Filicori, system chief of surgical innovation at Northwell Health, describes It in similarly practical terms. “I define physical AI as artificial intelligence that is connected to sensors, machines and the physical environment and can complete a closed loop of perception, planning and action,” he explained. “A physical AI system must perceive what is happening, understand the environment, decide what to do, act through a robot or device, and then adjust based on the result,” he said.
Complementary, Not Competing, Technologies
Filicori does not see generative AI as competing technologies. “Generative models can help physical systems understand instructions, generate training scenarios and reason about complex situations. World models add the ability to simulate what might happen next, connects those capabilities to action,” he observed.
Nvidia’s Perspective on Physical AI Healthcare Technology
David Niewolny, senior director of business development for healthcare and medtech at Nvidia, argues that hospitals should think of physical AI not as a replacement for today’s AI tools but as the next stage in their maturation. “It is the next wave of AI,” he said. “In healthcare, it connects intelligence to sensors, instruments, devices, rooms and robots, so AI can support work that happens around the patient, not only inside software.”
Why Physical AI Is Not “a Chatbot With Wheels”
“Physical AI is not a chatbot with wheels,” Niewolny explained. “It requires models that understand the physical environment, simulation that teaches systems how the world behaves, and edge computing that can respond in real time.”
Where Hospitals Will See Physical AI Healthcare Technology Value First
Although futuristic images of humanoid robots often dominate public discussion, all three experts expect healthcare’s earliest successes to come from much more practical applications. Martirosyan believes operational work presents the largest near-term opportunity. “Over the next five to ten years, I believe the largest immediate opportunity will come from bounded, repetitive tasks, not from trying to build a fully autonomous robotic doctor,” he predicted, pointing to transporting supplies, monitoring equipment availability, supporting patient navigation, and handling routine nonclinical requests as examples.
Logistics as the Fastest-Scaling Application
Filicori reaches much the same conclusion. “Over the next five years, logistics will probably scale fastest,” he said. “Supply delivery, pharmacy workflows, specimen transport, sterile processing, room turnover and inventory management involve repetitive, measurable tasks in relatively structured environments.” Niewolny likewise expects logistics, imaging assistance, patient monitoring and procedural support to become the first widely deployed use cases, noting that “physical AI can help close” the gap between healthcare demand and workforce capacity.
Why Clinical Care Represents the Longer-Term Opportunity for Physical AI Healthcare Technology
While logistics may arrive first, all three professionals believe the technology’s longer-term impact will reach much deeper into clinical care. Filicori points to surgery as one of the strongest candidates because robotic platforms already generate enormous amounts of structured data. “Surgery already has the essential foundation for physical AI: robotic platforms, high-resolution video, kinematic data, device telemetry and relatively structured workflows,” he explained.
Autonomy by Task, Not by Procedure
Rather than fully autonomous surgery, Filicori expects carefully bounded assistance. “I expect autonomy by task, not autonomy by procedure,” he said, a distinction he argues is likely to define healthcare’s broader approach to physical AI over the coming decade.
What This Physical AI Healthcare Technology Shift Means Going Forward
With Martirosyan, Filicori, and Niewolny all agreeing that hospitals waiting until physical AI becomes commonplace may find themselves scrambling to build the necessary infrastructure and governance, health systems evaluating this next AI frontier should begin considering the safety, oversight, and liability frameworks these technologies will require well before widespread deployment arrives. Given the consensus around logistics as the fastest-scaling near-term application, hospitals may find the clearest early ROI in supply transport, room turnover, and inventory management before extending physical AI into higher-stakes clinical applications like surgery.
What to Watch Going Forward
As this technology continues advancing through improvements in robotics, multimodal AI models, simulation, and edge computing, industry observers will likely watch for early physical AI deployments in hospital logistics to demonstrate measurable operational returns before more ambitious clinical applications, like task-specific surgical autonomy, gain traction. Part 2 of this series, exploring physical AI in greater depth, is set to publish the following day, and health system leaders considering their own physical AI healthcare technology strategy may find it a useful continuation of this foundational discussion around governance, safety, and near-term use cases.
For more healthcare industry updates, insights and news, visit DistilINFO. Click here to subscribe to stay informed.
