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Kate Gamble

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<h2>Key takeaways</h2>
UT Austin plans to break ground this fall on the UT Dell Medical Center, an AI-native hospital set to open in 2030 that embeds AI, robotics and sensor data into its infrastructure rather than layering them onto legacy systems. Core elements include an Intelligence Performance Center that merges clinical, supply chain and sensor data in real time, living digital twins meant to flag deterioration up to 72 hours ahead and robotics for medication delivery, sterile processing and surgical assistance. CHAI CEO Brian Anderson argues the model’s real value is centralized AI evaluation and governance, which lets health systems validate tools across sites and populations and then scale them to smaller and rural facilities that lack the resources to do it themselves.

The University of Texas at Austin breaks ground this fall on an AI-native hospital as part of its forthcoming UT Dell Medical Center. Set to open in 2030, the facility will be the first technology-integrated health institution of its kind in the U.S.

The term "AI-native" describes a system or facility built from the ground up with AI as a core component, rather than adding AI to established systems, as many healthcare systems are already doing. The new medical center, which is positioning itself as one of the most ambitious healthcare infrastructure projects in the country, will utilize environmental sensing tools to track patient conditions such as sleep patterns and stress levels. It will also use robotics for medication delivery, sterile processing, logistics, and surgical assistance, working in tandem with AI systems, which will control documentation, coding, and administrative processes. The hospital will also integrate the University of Texas MD Anderson Cancer Center, one of the top cancer hospitals in the U.S. 

Brian Anderson, MD, is CEO of the <a href="https://www.chai.org/">Coalition for Health AI</a> (CHAI). He says what sets the UT project apart is its potential to improve a hospital’s financial and operational workflows, not just clinical care. The model is built to show a health system where AI can raise efficiency and productivity while ensuring those tools are properly governed, validated and monitored.

In other words, it will help organizations identify where AI can improve efficiency and productivity, while making sure those tools are properly governed, validated, and monitored.

"Centralized evaluation gives local providers the assurance that a model will be safe, monitored, and effective across the specific, diverse patient population served by local or regional health centers," Anderson says. "For health systems with limited resources, that’s especially important. Ultimately, it’s about using AI to improve quality, efficiency, and productivity across the hospital, while maintaining patient trust and safety."

This new hospital is also being designed as a “greenfield” technology environment, meaning it will embed artificial intelligence, robotics, and data systems directly into its infrastructure from the ground up. A core feature of this technology will be its Intelligence Performance Center (IPC), which will integrate clinical, supply chain, and sensor data in real time. Another key feature of the hospital will be its “living digital twins." These computational models will be able to continuously update patient data to predict clinical deterioration up to 72 hours in advance, with the purpose of supporting early intervention and improving clinical decision-making.

Naturally, with AI being such a dominant component of this facility, patient security and protection is going to be a top priority and concern. According to Anderson, an AI-native hospital needs the infrastructure to test AI safely, govern how it’s used, and monitor its performance over time. That starts with a secure sandbox and the right guardrails, along with centralized governance, secure integration with systems like the electronic health record and strong protections for patient data, he says. 

"By combining strong governance and ongoing monitoring with collaboration and shared learning, we can build an environment where AI can be adopted responsibly and at scale, including in smaller and rural health systems," Anderson says. "What CHAI hears from our hospitals is that the goal is to move quickly enough to learn, while using near-real-time monitoring to manage risk and continuously adapt as the technology evolves, as moving too quickly never works out in anyone's favor."

UT aims to rank among the top 10 healthcare institutions in the U.S. within 10 years of opening. The project is backed by a $750 million gift from Michael and Susan Dell, and the university plans to raise $10 billion more. Anderson says the key is finding balanced centralized governance while moving quickly. That approach lets health systems evaluate AI across different sites and populations and build evidence about what works. From there, they can scale the effective tools to every site, including those with fewer resources.

"Ultimately, we want to make sure we’re protecting patients, clinicians, data, and operations while building the evidence and safeguards needed to use AI responsibly in clinical care," Anderson says.


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Emilia Benton is a Denver-based health and wellness journalist whose work has appeared in publications such as Runner's World, SELF, and Women's Health. She is also a 15-time marathoner, USATF Level 1-certified running coach, and an avid traveler.

UT Austin is building the U.S.'s first AI-native medical center. Here's what it will look like

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Digital twins, robotics and an Intelligence Performance Center anchor a 2030 facility backed by a $750 million Dell gift.

UT Austin is building the U.S.’s first AI-native medical center. Here’s what it will look like

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