The medical device industry is abuzz with advancements in artificial intelligence as AI-enabled tools redefine healthcare processes. In 2024, major players like GE Healthcare, Medtronic, and Dexcom introduced groundbreaking AI integrations, while companies such as Stryker and Quest Diagnostics expanded their capabilities through strategic acquisitions. Amid the buzz, evolving regulations and the potential of generative AI took center stage at industry conferences.
In 2025, AI in MedTech continues its transformative journey, with regulatory clarity, adoption challenges, and innovative applications reshaping the sector. Here are four crucial trends driving this evolution.
Regulatory Clarity Boosts AI Development
The FDA has taken significant steps to guide developers of AI-enabled devices. In late 2024, the agency finalized its framework on pre-determined change control plans (PCCPs), enabling pre-approved modifications for devices post-market. This framework marks a major shift, as companies can plan updates in advance, reducing the burden of postmarket submissions.
Amanda Johnston, a legal expert specializing in FDA compliance, predicts a surge in PCCP submissions. “I see this becoming a focal point for the FDA, encouraging developers to adopt the framework to streamline processes,” Johnston said.
The draft guidance issued in January 2025 further clarified the information required for AI device submissions, including the importance of PCCPs and postmarket monitoring. Developers are urged to use these guidelines as a checklist when preparing their submissions. Megan Robertson, an attorney at Epstein Becker Green, described the draft guidance as a critical resource for ensuring compliance with FDA expectations.
However, uncertainty looms as the Trump administration shapes its stance on AI regulation. With the rescission of a previous AI-focused executive order and changes in leadership at the Department of Health and Human Services (HHS), industry leaders are closely watching how these developments will impact the future of AI in MedTech.
Insurance Coverage: A Barrier to AI Adoption
Despite its potential to revolutionize healthcare, AI in MedTech faces hurdles when it comes to reimbursement. The Centers for Medicare and Medicaid Services (CMS) currently lacks specific payment pathways for FDA-authorized AI technologies, leaving developers reliant on temporary workarounds like the New Technology Add-on Payments program.
In 2024, bipartisan efforts pushed for broader reimbursement options. A task force report highlighted CMS’s limited Medicare coverage for AI-based services, sparking discussions about establishing permanent payment pathways.
Hospitals, under mounting financial pressure, are scrutinizing AI tools more carefully. According to Brian Anderson, CEO of the Coalition for Health AI (CHAI), there’s a growing demand for clear evidence of return on investment. Anderson stated that health systems are shifting from relying solely on hype to requiring tangible benefits from AI solutions.
As developers work to demonstrate the value of AI tools in reducing operational costs and improving outcomes, resolving payment challenges will be crucial for wider adoption.
Foundation Models and Administrative AI Tools Gain Momentum
Foundation models—large-scale AI models with multiple applications—are gaining traction in MedTech. While most FDA-regulated AI tools currently focus on radiology, companies are expanding their reach into pathology, cardiology, and administrative tasks like clinical note generation.
Large language models, such as those developed by Rad AI, are already being used to summarize clinical findings into reports. Although these models are not yet regulated as medical devices, vision-language models that analyze medical images and draft reports are under development, with FDA authorization anticipated in the near future.
Dr. Nina Kottler, a clinical AI expert at Radiology Partners, noted the shift from single-use AI tools to solutions that address workflow challenges. “The mismatch between workload and capacity has driven the demand for tools that enhance efficiency,” she explained.
Despite their promise, foundation models require rigorous evaluation. AI tools must prove their ability to adapt to diverse healthcare environments, and developers must address concerns about performance variability and potential biases.
AI Governance: A Growing Concern for Hospitals
As hospitals increasingly adopt AI tools, questions about governance, model maintenance, and performance monitoring are coming to the forefront. A study in Health Affairs revealed that only 61% of hospitals using predictive models test them on their own data, raising concerns about reliability and bias.
Scott Lucas, vice president of device safety at ECRI, stressed the need for proper governance in AI tools to prevent preventable errors and ensure hospitals have the necessary resources and expertise to monitor them over time.
Radiology Partners has developed a five-step process to evaluate AI models before implementation, focusing on factors like accuracy, error rates, and real-world applicability. “A model that simply replicates what a radiologist can already do isn’t worth the investment,” Dr. Kottler said.
To improve transparency, organizations like CHAI are advocating for “model cards” that provide detailed information about an AI tool’s training data and performance metrics. These cards, along with assurance labs that evaluate AI models across diverse populations, aim to equip hospitals with the knowledge needed for informed decision-making.
The Road Ahead for AI in MedTech
AI in MedTech is poised for remarkable growth in 2025, driven by advancements in regulatory clarity, foundation models, and governance frameworks. However, overcoming barriers like insurance coverage and ensuring robust evaluation processes will be critical to realizing its full potential.
As the industry navigates these challenges, one thing is clear: artificial intelligence will continue to shape the future of healthcare, offering innovative solutions to improve patient outcomes and operational efficiency.
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