Faisal Zain is a leading expert in medical technology, known for his work at the intersection of healthcare innovation and public policy. As a key figure in a new AI initiative at the Paragon Health Institute, a think tank with close ties to the Trump administration, he is shaping the conversation on how artificial intelligence will transform American healthcare. His work focuses on developing market-based policy recommendations that aim to lower costs, eliminate fraud, and accelerate technological progress.
This interview explores the core tenets of his approach: fostering innovation through a light-touch regulatory framework, navigating the complex political landscape of technology policy, and building effective public-private partnerships. We delve into the practical challenges of creating a unified national strategy for health AI, the delicate balance between private sector interests and the public good, and the strategies used to build bipartisan consensus in a deeply divided political environment.
Your initiative aims to lower healthcare costs and root out fraud using AI while avoiding overly restrictive rules. What does a market-based, light-touch regulatory approach look like in practice? Please share a step-by-step example of how this would foster innovation without compromising patient safety.
That’s the central challenge, isn’t it? Resisting the urge to regulate with a sledgehammer. In practice, a light-touch approach means we define the destination but don’t micromanage the journey. For instance, let’s take the massive problem of waste and abuse in our healthcare system. Instead of creating a thousand-page rulebook detailing how an AI algorithm must be structured, we would start by clearly defining the problem for the private sector: “Here is a secure, anonymized dataset of five million medical claims. We need a tool that can identify fraudulent billing patterns with 99% accuracy.” The next step is to create a regulatory sandbox. Companies, from startups to giants, can deploy their solutions in this controlled environment. The government’s role isn’t to pre-approve every line of code but to monitor the outcomes in real-time. Finally, we would focus on post-deployment monitoring. If a tool is successful in the sandbox, it gets a provisional green light for wider use, but with the requirement that its performance is continuously reported. This way, innovation isn’t stifled by a bureaucratic checklist upfront; it’s guided by real-world results and a commitment to balancing efficiency with existing legal and privacy frameworks.
A patchwork of different state AI laws could create significant compliance challenges for companies. What are the biggest risks this scenario poses to innovation, and what specific federal policies could create a more unified framework without stifling progress? Please describe how you’d measure success.
A patchwork of state laws is one of the greatest threats to the healthcare transformation we’re trying to achieve. The biggest risk is that it chokes off innovation before it can even start. Imagine a brilliant small company in Texas develops a groundbreaking AI diagnostic tool. If they have to navigate 50 different, and potentially conflicting, sets of rules for data privacy, liability, and transparency, the compliance costs become astronomical. They would need a team of lawyers just to get to market, which means only the largest corporations could compete, and many life-saving ideas would die on the vine. To create a unified framework, the federal government should focus on setting a national baseline—a floor, not a ceiling. This would involve establishing clear, consistent standards for data de-identification and interoperability, and creating safe harbor provisions for developers who adhere to these best practices. Success wouldn’t be measured by the thickness of the rulebook, but by the vibrancy of the market. We’d measure success by tracking the number of new AI health tools that achieve nationwide approval each year and a decrease in the average time it takes for a new technology to go from development to clinical use.
Future administrations may favor public-private collaborations over traditional rulemaking for AI. What would an ideal collaboration between government health agencies and private tech companies look like? Could you describe the specific roles each party would play and what metrics would define a successful partnership?
An ideal collaboration is a true partnership, not a simple vendor-client relationship. The government, especially agencies like the FDA, has already expressed concern about its own limitations in maintaining exhaustive oversight on something as dynamic as AI. So, their role shifts from being a rigid gatekeeper to a strategic convener and goal-setter. They would define the critical public health challenges—for example, “We need to predict the next pandemic threat” or “We need to cut the time for clinical trials in half.” The private sector’s role is to bring its agility, technical expertise, and capital to the table to build the solutions. I see this happening through targeted grants or challenge-based initiatives. A successful partnership would be defined by concrete metrics: a measurable reduction in drug development costs, a significant increase in the speed of diagnostic analysis, or a quantifiable drop in Medicare fraud. Success is also seeing the best minds from the tech field actively engaging and debating with regulators, creating a feedback loop where policy is informed by on-the-ground innovation, not written in a vacuum.
Given the political division surrounding technology, how do you plan to make your policy recommendations appealing across the aisle? When educating skeptical lawmakers, what key trade-offs between regulation and innovation do you emphasize? Can you share an anecdote from one of these discussions?
The key is to frame the conversation around shared goals. No one, regardless of party, wants healthcare to be more expensive, less efficient, or less safe. I always start from that common ground. The main trade-off I emphasize is not between regulation and no regulation, but between smart, adaptive oversight and slow, rigid rulemaking. I often tell lawmakers that the “sloppy cowboy approach” of just letting technology run wild is not an option; we have to clean up the mess afterward, and that’s a bad outcome for everyone. But I also remind them that a system that tries to eliminate all risk upfront through heavy-handed rules will also eliminate all progress. I was recently in a meeting where a regulator was deeply concerned about AI bias. Instead of dismissing the concern, I acknowledged it and reframed it: “Our current human-based system is full of biases that are incredibly hard to measure or correct. With AI, we can audit for bias in a systematic way and actively work to mitigate it.” We’re not chasing a flawless system, but a demonstrably better and more transparent one. That focus on measurable improvement, rather than an impossible standard of perfection, tends to resonate on both sides.
Your work involves collaborating with industry leaders from companies like UnitedHealth Group and also meeting directly with government regulators. How do you balance the interests of the private sector with the public good? Walk me through your process for turning an idea into a concrete policy proposal.
It’s a constant balancing act, but it’s achievable because, ultimately, a chaotic and untrustworthy market serves no one’s long-term interests. My process begins with collaboration. It starts with identifying a real-world friction point by talking with people in the field, like my work with Michael Pencina at UnitedHealth Group. For example, we saw a clear need for guidance on what happens after an AI model is deployed. That idea then moves into a research phase, where we analyze existing legal frameworks and technological capabilities. From there, we draft a concrete policy proposal, often in the form of a detailed paper, that is grounded in both technical reality and sound policy principles. The final, crucial step is taking that paper directly to government regulators. By showing up with a well-researched, co-authored proposal, we aren’t just asking them to solve a problem; we’re presenting a viable, collaborative solution that already has buy-in from industry leaders. This approach transforms the dynamic from an adversarial one to a partnership focused on a shared objective: leveraging technology to build a better, more efficient healthcare system for everyone.
What is your forecast for the integration of AI into the U.S. healthcare system over the next four years?
My forecast is one of accelerated, pragmatic adoption, driven less by sweeping legislation and more by targeted, strategic partnerships. I believe the administration understands that AI is fundamentally changing the game and that traditional, slow-moving notice-and-comment rulemaking is ill-suited for this moment. Instead, you’re going to see a surge in public-private collaborations and the use of government grants to solve specific health AI challenges. The government will increasingly reach out to the big names in the tech ecosystem, not to dictate terms, but to ask, “How can we innovate together?” We will see AI move beyond the pilot stage and become embedded in core healthcare functions like fraud detection, claims processing, and post-deployment monitoring of new technologies. It won’t be a sudden, dramatic overhaul, but a steady and powerful integration that makes our system smarter, faster, and more resilient. The focus will be on tangible results and building a framework that encourages the best minds in the field to help transform American healthcare.
