HOSPITALS
David points to significant progress in Taiwan to advance the transition. For example, Foxconn and other leading medical centres have moved beyond pilot programmes to deploy coordinated AI agents, collaborative robots and digital twins to support everything from cancer screening and ECG analysis to surgical workflows and hospital operations.
A changing macro landscape David believes three core forces are converging to expedite the development of AI-native hospitals: healthcare capacity limitations, AI maturity and simulation advances.
“ Around the world, health systems are dealing with staffing shortages, ageing populations and rising healthcare costs, so the pressure to do more with less is very real,” says David.
Adding to this practical need, large language models, agentic AI, accelerated computing, simulation and physical AI have reached a stage of maturity where they’ re advanced enough to deploy in real healthcare settings.
David explains,“ We’ re seeing hospitals use digital twins to safely train, test and validate new workflows before they ever reach a live clinical environment, which helps accelerate innovation while reducing risk. Digital twins let hospitals rehearse change before patients ever experience it.” Hospitals and clinicians have also become increasingly willing to evaluate and deploy these technologies than they were previously.
According to Nvidia’ s“ State of AI in Healthcare and Life Sciences: 2026 Trends” report:
60 September 2026