Convergence of need technology and culture driving the era of physical AI in medicine

Abstract

Medicine is a predominantly physical profession, yet most medical artificial intelligence (AI) remains screen bound. Physical artificial intelligence (PAI) extends AI's capabilities and can directly address many of the healthcare system challenges and unmet needs such as workforce shortages, unsustainable healthcare costs, heightened expectations, aging population and need for pandemic preparedness. PAI relies on systems that autonomously perceive, decide, and actuate in real-time by synthesizing diverse environmental data from multiple sensory sources. These data undergo rapid processing and interpretation, facilitating immediate decision-making and responsive physical actions, including movement, object manipulation, and direct human interaction. This paper first identifies critical healthcare system needs that robotic agents can effectively address. It further examines core actions of PAI systems, emphasizing perceptual pathways, clinical decision-making processes, and human-robot interactions that translate sensory inputs into tailored, patient-specific responses. We explore essential data utilization aspects, including edge-device advances, PAI datasets, digital twins, and edge-to-cloud infrastructures that support real-time inference and are crucial for reducing barriers to PAI implementation. Finally, we analyze the cultural factors accelerating the adoption of PAI in healthcare and review PAI advancements in other sectors. We argue that the convergence of pressing healthcare demands, technological advancements, and cultural readiness signals a tipping point for PAI in medicine.