Key Takeaways:
- The health care industry is preparing for a debate over how clinical artificial intelligence (AI) should be paid for, with many AI-enabled medical devices lacking payment from insurers.
- The American Medical Association (AMA) has authorized only three AI devices with a permanent CPT code, but many more are waiting in the wings.
- The Centers for Medicare and Medicaid Services (CMS) is grappling with its own payment paradigms for AI, and senators have proposed legislation for formalizing AI payment pathways.
- Health systems are looking for ways to pay for AI, with some using fee-for-service models, while others are experimenting with value-based payment models.
- The use of AI in breast imaging and opportunistic screening is becoming more prevalent, with some health systems implementing self-pay models or using existing radiological images to screen for health conditions.
Introduction to the Debate
The health care industry is gearing up for a battle over whether and how clinical artificial intelligence should get paid for. As of the end of September, the Food and Drug Administration has authorized 1,357 AI-enabled medical devices, but very few of those tools are actively paid for by insurers. According to Ateev Mehrotra, chair of health services, policy, and practice at the Brown University School of Public Health, "With AI, so much of the conversation is about how do we get paid for the individual technology. If I could wave a magic wand, I would change our paradigm to: ‘How can we use AI to improve the productivity and efficiency of clinicians so that they can care for more patients at a high quality, at a lower cost?’"
The Payment Paradigm
Others, especially in industry, worry that lack of payment will stymie adoption and prevent helpful AI from reaching patients. In 2026, the debate over AI payment will intensify as more devices enter the field. The AMA has doled out more than 20 temporary category III codes to AI, many of which will eventually become permanent. As Pelu Tran, CEO of AI governance company Ferrum Health, noted, "health systems are looking at AI and wondering how the heck they pay for this." The CMS is also grappling with its own payment paradigms for AI, which have largely relied on vendors’ own valuation of their software.
Fee-for-Service Models
One example of a fee-for-service model is the use of AI to analyze coronary plaque in patients. In January, Medicare will start paying physicians a set national rate of just over $1,000 for using AI that analyzes the type and quantity of plaque in a patient’s coronary arteries. As Eric Rubin, primary CPT adviser for the American College of Radiology, said, "We’re just now at a place where we’re seeing close to universal payments of the procedure." However, payment for plaque analysis has been "very spotty," according to Rubin, and it will be important to see how this progresses over time.
Self-Pay Models
In contrast, some health systems are using self-pay models for AI-based breast imaging. Across the country, more women going in for their annual mammogram now have the option to select an AI add-on to highlight suspicious lesions, but without reimbursement from insurers, it’s usually the patients who pay up – usually around $40 to $50. As Greg Sorensen, chief science officer at large outpatient imaging company RadNet, noted, "All of us physicians felt like moving to a self-pay model was not our preferred approach. We would have preferred that payers would have embraced this right from the start."
Value-Based Experiments
In the absence of reimbursement or patient payment, health systems can still choose to invest in AI. They just have to believe that the technology will improve efficiency or health care quality enough to be worth it. As Mehrotra said, "If they judge based on the published literature or their own internal experience that this is adding enough value for them, then I think that we should use that as a sign, because they’re paying out of their own pocket." Several health systems are implementing opportunistic screening for health conditions using existing radiological images, such as NYU Langone’s experiment using CT scans to look for signs of osteoporosis.
Opportunistic Screening
Opportunistic screening programs are typically implemented as part of clinical research to determine whether catching risky signs in images actually improves patient outcomes. As Hari Trivedi, co-director of the Health Innovation and Translational Informatics lab at Emory University, noted, "I like to call opportunistic screening this rare triple win in the U.S. health care system, especially for AI." Patients get their health risks caught earlier, health systems can capture more revenue from patients referred for follow-up preventive care, and payers can save money in the long term by preventing hospitalizations from major health events.
Who will pay for AI in health care? 3 trends to watch in 2026