Physical AI is artificial intelligence that can sense, reason, and act in the physical world through robots, devices, instruments, and automated systems. In healthcare, that means AI is no longer limited to software that analyzes records or images; it is also showing up in surgical systems, rehabilitation platforms, wearables, smart medical devices, hospital robots, and automated laboratory infrastructure. There are several motivations behind writing this guide: 1. The increasing number of Pitch3D startups is now deploying physical AI to build or defend their existing products. 2. An increasing number of incumbent AI giants, including leading companies like NVIDIA, or venture capital firms, believe the next phase of AI-driven growth will be from this category of technologies. In part one of this guide, we focus on general concepts and players in current healthcare and life science sectors.
What Physical AI Is and Is Not?
While most well-known examples of physical AI applications are autonomous vehicles and robots, digging a little deeper into this new concept will expand the reader’s visual field to fields far beyond these two examples, and perhaps inspire innovation.
There are many definitions out there, but here is one I have concluded based on several references:
Physical AI is an integrated software-and-hardware system that can perceive and understand the real world (think objects, spatial relationships, dynamics, constraints, cause-and-effect, etc.). It then uses reasoning to adapt actions that would complete tasks and interact with the real world.
Perception. Interpretation. Action.
These three words sum up the core of physical AI. However, systems that rely primarily on fixed rules, task-specific processes, or manually engineered world models do not qualify as physical AI according to IQT [1].
Many industrial robots, such as those in an automobile production line, are in this category. True physical AI hardware or software does not rely on pre-defined signals or programming.
Why Physical AI matters in healthcare?
Healthcare is full of physical workflows: clinicians perform procedures, patients interact with devices, laboratories handle samples, and manufacturers run tightly controlled production systems. Physical AI matters because it brings intelligence into real-world processes by combining sensing, decision-making, and action, often under strict constraints on safety, reliability, timing, and regulatory requirements in healthcare systems.
This shift is important because many of the biggest opportunities in medicine are not purely digital. Globally, healthcare is in crisis. Here in the U.S., this crisis is driven by an increasing gap between supply and demand due to a healthcare labor shortage and an exponential rise in healthcare costs. Many believe that better technologies are our salvation to solve this crisis. However, better care often depends on how well these new technologies are delivering. For example, how well a robot assists a surgeon, how accurately a wearable detects a change in physiology, how safely an exoskeleton adapts to a patient’s gait, or how efficiently a lab automation system handles samples and experiments. Many now believe and actively invest in physical AI, hoping this will unlock the potential of existing healthcare technologies.
What are the major categories of physical AI?
A practical way to understand physical AI is to group it by the type of system involved.
Embodied robotics
Embodied robotics includes systems that move and manipulate the world, such as surgical robots, rehabilitation robots, exoskeletons, and other intelligent machines that use sensors and AI models to perceive environments and execute tasks. In healthcare, this is the most visible facet of physical AI because these systems directly support procedures, therapy, and mobility. They interact with patients and providers directly.
Robotic surgical platforms are one of the clearest examples. These systems combine imaging, motion control, sensing, and AI-assisted guidance to improve precision, dexterity, and workflow during minimally invasive procedures.
Many would agree that Intuitive Surgical has a near monopoly in this sector. However, this landscape is changing rapidly with more flexible and cheaper systems like CMR Surgical and Moon Surgical now available to financially constrained healthcare buyers. Many major medical device companies are investing heavily in robotics, ranging from internal initiatives to M&A. Almost all orthopedic device companies now own surgical robotics divisions and surgical planning software as part of the device offering.
Autonomous mobile systems
This category covers robots and autonomous platforms that navigate physical spaces, avoid obstacles, and complete transport or inspection tasks. In hospitals and health systems, examples include logistics robots that move medications, meals, linens, or specimens between departments. Swisslog Healthcare, Diligent Robotics, Ottonomy, and Aethon are just a few examples in this operational layer of healthcare automation.
Sensor-driven smart devices
Many physical AI systems in healthcare are not robots at all. They are devices that sense physiological signals, interpret them, and adjust therapy or guidance in real time. Examples include wearables, implants, monitoring tools, and therapeutic devices that continuously interpret physiological or environmental data and adjust behavior accordingly. This is one of the most important categories in healthcare because many high-value products, from closed-loop insulin systems to connected respiratory devices, sit at the intersection of embedded sensing and real-time AI. Medtronic, GE Healthcare, Siemens Healthineers, Abbott, Philips, ResMed, and Omron illustrate the range of companies embedding AI into imaging systems, chronic disease management, remote monitoring, and home care devices. Some consumer-based wearables, like the Oura ring and the Eight Sleep, are also popular smart devices.
Industrial and lab automation
Physical AI also includes intelligent automation in laboratories and biomanufacturing settings, where robotic handlers, liquid-handling systems, imaging stations, and quality-control systems coordinate physical workflows. In life sciences, this category matters because scientific progress increasingly depends on automating repetitive experimental tasks while preserving precision and traceability. That said, many have failed to achieve this “automation of biology” in the past decades, and it is worthwhile to pause and reflect upon this. One major reason for such failure is perhaps due to trying to use deterministic engineering systems to interact with a noisy, context-dependent, dynamic, and complex biological system. Biology would require physical AI to achieve real-time perception, interpretation of complex systems and signals, and adaptation.
Some of the additional bottlenecks that are open to innovations also include:
- Tacit and Unspoken Knowledge: A significant source of failure lies in the disconnect between biologists and automation engineers. Unwritten, “feel-based” bench protocols (like precise mixing or cell handling) are incredibly difficult to translate into rigid code. (An LLM could be useful to enabling scientists and AI to collaborate to optimize protocols.)
- The “Black Box” of Biological Data: Generating more data through automation doesn’t equate to understanding if the data analysis pipelines are not equally advanced. Biology lacks the unified, open-source ecosystems (like Python/GitHub) found in traditional software development, leaving methods fragmented.
- Lack of Mass Standardization: Unlike next-generation sequencing, which has collapsed in cost due to miniaturization, many general cellular assays remain highly expensive and rigid, depriving automated pipelines of affordable inputs.
Thermo Fisher Scientific, Danaher, Agilent, Tecan, Hamilton, Sartorius, Cytiva, Azenta, HighRes Biosolutions, and Beckman Colter are among the companies building the hardware, robotics, and orchestration layers that turn laboratories and production environments into more intelligent physical systems.
Rehabilitation robots and assistive devices
Rehabilitation and assistive technologies form a distinct category because they combine sensing, adaptation, and direct interaction with the human body. These systems include gait trainers, neurorehabilitation robots, exoskeletons, smart prosthetics, and adaptive orthotics that tailor support to the user’s movement and condition. Pysonic, a San Diego-based company, offers highly durable, touch-sensing robotic arm prosthetics to both amputees and large industrial manufacturers. Companies such as Ekso Bionics, ReWalk Robotics, and Fourier Intelligence illustrate how physical AI can directly influence recovery, training intensity, and day-to-day mobility support.
Where Pitch3D startups fit?
The 3DHEALS Pitch3D program focuses on fundraising in healthcare 3D printing and bioprinting, but its scope also includes adjacent technologies such as AI and machine learning, robotics, workflow software, 3D scanning, VR and AR, and data-driven planning tools. That makes Pitch3D a useful lens for understanding how physical AI overlaps with healthcare 3D technologies.
Not every Pitch3D company should be labeled a physical AI startup. Some are primarily materials, printing, or manufacturing businesses. A practical test is whether the company connects intelligent perception or planning to a real-world physical intervention, such as a surgical workflow, a patient-specific implant, a smart device, or an automated production process.
Some of the recent Pitch3D startups stand out as physical-AI-adjacent because they connect digital models to physical care delivery:
Psyonic– uses physical AI by combining high‑speed motor control, multi‑touch sensing, and intelligent signal interpretation in its Ability Hand, creating a bionic prosthesis that can feel, adapt, and perform dexterous physical actions for both humans and robots.
Carlsmed– uses physical AI to turn imaging and outcomes data into personalized 3D‑planned spinal surgeries and patient‑specific implants that precisely match each patient’s anatomy and alignment.
Cosm – uses physical AI by combining pelvic ultrasound imaging, AI‑driven cloud software, and 3D printing to design and manufacture personalized pessary devices that precisely match each patient’s anatomy and pelvic floor dynamics.
Vent Creativity– uses physical AI by creating AI‑powered digital‑twin bone and joint models that simulate forces and motion, enabling FDA‑cleared 3D surgical planning tools that directly inform implant design and surgical execution
Zylo3D – uses physical AI in digital dentistry by combining advanced 3D printing hardware with intelligent workflows that let dental offices produce patient‑specific prosthetics and devices on‑site, tightly coupling design and fabrication to real mouths
These companies are especially interesting because they show that physical AI in healthcare is not limited to humanoid robots or autonomous hospital machines. It also includes systems that transform imaging, planning, and patient-specific data into physical devices, surgical tools, implants, and treatment workflows.
Challenges that define this market
The concepts of AI and robotics are decades-old and century-old ideas, respectively. However, while current AI enthusiasm is mainly driven by advances in large-language models, achieving physical AI will be even more challenging and expensive. Hence, why Jensen Huang is so excited about physical AI as the next frontier, anticipating an even larger-scale economic windfall if society truly buys into this vision.
Physical AI in healthcare faces an even higher bar than consumer or enterprise AI because it operates in environments where errors can directly affect patient outcomes. Negative patient outcomes from AI operations in healthcare systems may create an even higher psychological barrier to future adoption in an already conservative industry. Safety, validation, human oversight, interoperability, latency, and regulatory compliance are therefore central design requirements rather than afterthoughts.
Another challenge is that many healthcare startups still operate in narrow workflow slices. A company may have strong AI for imaging or design but limited physical automation, or strong hardware with limited intelligence. The most durable category leaders will likely be those that integrate perception, control, workflow software, and regulatory execution into coherent end-to-end systems.
What to watch next?
The next phase of physical AI in healthcare will likely be shaped by three converging trends. First, robots and devices will become more adaptive and agentic; second, labs and factories will become more software-orchestrated; and third, 3D design, simulation, and world models will become more central to medical product development and procedural planning.
For healthcare innovators, investors, and startup founders, the most important question is no longer whether AI belongs in medicine. The better question is where intelligence should live in the physical workflow: in the robot, in the implant design process, in the smart device, in the rehabilitation platform, or in the automated lab system that enables the next breakthrough. It seems that we are only limited by our imagination.
References:
- https://www.iqt.org/library/what-is-physical-ai-a-definition-and-framework
- https://www.ibm.com/think/topics/physical-ai
- https://www.synopsys.com/glossary/what-is-physical-ai.html
- https://www.hpe.com/us/en/what-is/physical-ai.html
- https://www.medtechdive.com/news/fda-ai-medical-devices-growth/728975/
- https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-and-Global-Robotics-Leaders-Take-Physical-AI-to-the-Real-World/
- https://blogs.nvidia.com/blog/ai-medical-devices-gtc-2024/
- https://intuitionlabs.ai/articles/top-20-medtech-companies-using-ai-2025
- https://3dheals.com/pitch3d/
- https://pmc.ncbi.nlm.nih.gov/articles/PMC10390055/
Comments