The healthcare landscape is undergoing a significant transformation as the Centers for Medicare & Medicaid Services (CMS) accelerates the integration of artificial intelligence (AI) into clinical care. This shift is part of a broader strategy to enhance patient outcomes and streamline healthcare delivery through innovative technologies.
At a recent healthcare AI event hosted by the Consumer Technology Association (CTA) in Washington, D.C, Stephanie Carlton, deputy administrator at CMS and chief clinical AI officer, outlined the agency’s evolving AI strategy. This strategy aims to establish clearer pathways for AI market access and regulation, while also exploring reimbursement frameworks for AI-enabled technologies.
CMS’s AI Strategy: Four Key Pillars
CMS’s AI strategy is built on four primary pillars: building public trust, expanding healthcare data sharing and interoperability, establishing clearer pathways for AI market access and regulation, and developing reimbursement frameworks for AI-enabled technologies. These pillars are designed to create a robust ecosystem for AI in healthcare, ensuring that the technology is safe, effective, and accessible.
Carlton emphasized the importance of these pillars in fostering a supportive environment for AI innovation. “We are committed to an outcomes framework for patients,” she noted, highlighting the agency’s focus on total cost of care and measurable health outcomes.
The ACCESS Model: A New Approach to Chronic Care Management
A centerpiece of CMS’s AI strategy is the ACCESS model—Advancing Chronic Care with Effective, Scalable Solutions. Launched in, this 10-year program focuses on value-based chronic condition management, leveraging technology and AI to scale care for large populations of patients.
The ACCESS model is currently being tested with over 150 healthcare organizations, providing predictable, recurring, outcomes-based payments for technology used to treat conditions such as diabetes, hypertension, chronic kidney disease, obesity, depression, and anxiety. This model serves as an early test of whether AI tools can improve measurable health outcomes and lay the groundwork for a broader role in managing total cost of care.
Carlton hinted at upcoming expansions to the ACCESS model, stating that the team has been working carefully on additional tracks. “We’re still committed to an outcomes framework for patients,” she reiterated, emphasizing the agency’s dedication to this approach.
Regulatory Frameworks and Future Directions
As the industry moves from general wellness and clinical decision support into higher-risk AI functions, CMS is exploring how to regulate and deploy increasingly sophisticated AI tools. Carlton pointed to state-level pilot programs as one avenue for evaluating higher-risk use cases.
CMS is also considering how to develop reimbursement frameworks for these technologies. “When do we pay for it? When does that make sense?” Carlton questioned, highlighting the need to differentiate between general apps and technologies performing medical functions that are reasonable and necessary for Medical care.
The Food and Drug Administration (FDA) is also playing a crucial role in this regulatory landscape. In August 2026, the FDA issued a discussion paper on regulatory considerations for generative AI-enabled medical devices, seeking feedback on how to assess risks, evaluate safety and effectiveness, and monitor device performance after deployment.
The discussion paper outlines a possible framework for evaluating GenAI-based medical devices based on two factors: how independently they act and the potential harm from incorrect outputs. The FDA is considering a “competency-based” evaluation framework, combining extensive benchmarking with real-world clinical validation.
Carlton compared the discussion paper’s concept of benchmarking to a doctor earning a medical degree by demonstrating core competencies, and likened clinical confirmation to a medical residency where AI tools are evaluated in real-world settings to ensure they are safe, effective, and ready for broader use.
Rick Abramson, M.D, associate director for digital health and director of the Digital Health Center of Excellence in the FDA’s Center for Devices and Radiological Health, noted that the current review framework is not well suited to generative AI. “I’m absolutely confident that the evidentiary standard for FDA authorization of generative AI tools will change,” he said, emphasizing the need for industry contribution in advancing regulatory approaches.
As regulators sketch out the roadmap for healthcare AI regulation and policy, physicians and health tech executives continue to debate the opportunities and risks of AI in real-world medical practice. The discussion often revolves around the potential use of fully autonomous AI versus human-in-the-loop clinical care.
Jesse Ehrenfeld, M.D, past president of the American Medical Association, noted that even in the case of AI-enabled prescription refills, there are situations where a human physician’s cognitive judgment is necessary to ensure safe patient care. “There are pieces of that that are just impossible to automate,” he said, emphasizing the importance of marrying the best of both AI and human intelligence.
Marc Paradis, principal and founder of SIYOM Consulting, argued that discussions should focus on AI’s future potential rather than its present limitations. He suggested that AI will soon go beyond today’s text-based large language models and will be able to detect subtle clinical signals, such as changes in voice, tremor, or other physiological markers.
John Whyte, M.D, CEO of the American Medical Association, pushed back, arguing that AI and human intelligence are fundamentally different and cautioning against overstating AI’s capabilities without strong evidence. “What we should be talking about is what is the evidence base that we need to make decisions as it relates to safety, as it relates to patient outcomes,” he said.
Laura Adams, senior advisor at the National Academy of Medicine, challenged the common assumption that clinicians should always remain “in the loop” when AI is used in patient care. She argued that requiring physician review of every AI decision could create unnecessary delays and inefficiencies, particularly in cases where AI has already demonstrated strong performance, such as image analysis.
The goal, she argued, should be to determine when clinician involvement meaningfully improves outcomes and when it may actually reduce the benefits of AI.
