AI in Medtech: Regulatory Clarity and Adoption Challenges in 2025

The medical device industry continues to grapple with the rapid integration of artificial intelligence (AI) technologies, as recent regulatory developments and market dynamics shape the landscape. Key players like GE Healthcare, Medtronic, and Dexcom are at the forefront of AI implementation, while regulatory bodies and healthcare providers navigate the complexities of this evolving field.

FDA Guidance Provides Clarity for AI Device Developers

The U.S. Food and Drug Administration (FDA) has taken significant steps to clarify its approach to AI-enabled medical devices. In December 2024, the agency finalized guidance on pre-determined change control plans (PCCPs), offering a framework for pre-specified modifications to devices post-market. This was followed by draft guidance in January 2025, outlining submission requirements for AI devices and postmarket monitoring needs.

Amanda Johnston, a partner at FDA-focused law firm Gardner, expects increased adoption of PCCPs, noting, "I see some of that being a request on the FDA's end. I do think that they will try to push developers into that framework." While PCCPs require more upfront work, they can potentially save time and money on postmarket submissions.

The regulatory landscape remains in flux, however, as the Trump administration's approach to AI regulation is yet to be fully defined. Martin Makary, nominated for FDA commissioner, has shared Scott Gottlieb's call for Congress to update FDA regulations on medical AI, signaling potential changes ahead.

Reimbursement Challenges Persist for AI Technologies

Despite the growing integration of AI in medical devices, reimbursement remains a significant hurdle. The Centers for Medicare and Medicaid Services (CMS) currently lacks specific reimbursement pathways for FDA-authorized AI technology, forcing companies to rely on workarounds like Medicare's New Technology Add-on Payments.

BTIG analyst Ryan Zimmerman notes that companies are pitching AI features to hospitals as a means to streamline processes and alleviate staffing pressures. However, Brian Anderson, CEO of the Coalition for Health AI (CHAI), observes a more cautious approach from healthcare providers: "Now we're seeing a little bit of a sobering perspective that if we're going to be spending a large amount of capital to purchase these things, we need to make sure that we're seeing economic return on investment."

Evolving AI Applications and Evaluation Challenges

The medical AI landscape is rapidly diversifying beyond traditional radiology applications. Foundation models and administrative tools are gaining traction, with companies like GE Healthcare developing large-scale models for various purposes, including MRI image processing and electronic health record analysis.

Nina Kottler, associate chief medical officer of clinical AI for Radiology Partners, highlights the shift in AI solutions: "This gap between volume and capacity has been growing for years. It is so great that it takes over any other use case you'll consider." Kottler emphasizes the importance of evaluating AI models on local data, as FDA-submitted performance data may not generalize well across different practices.

As AI devices become more prevalent, healthcare institutions face challenges in governance and performance monitoring. Scott Lucas, ECRI's vice president of device safety, expresses concern about the rapid evolution of AI tools in an environment prone to preventable incidents. A recent Health Affairs study found that only 61% of hospitals using predictive models tested them on their own data, underscoring the need for more rigorous evaluation practices.

The medtech industry stands at a critical juncture, balancing the promise of AI with regulatory, financial, and practical challenges. As stakeholders work to address these issues, the coming years will likely see continued innovation and refinement in the application of AI to healthcare.