The Shocking Cost of AI in Healthcare: 3 Game-Changing Trends You MUST Know Before 2026!

The health care industry is on the cusp of a significant clash over how to manage the integration and reimbursement of clinical artificial intelligence (AI) technologies. As of late September, the Food and Drug Administration (FDA) has authorized a staggering 1,357 AI-enabled medical devices. However, the reality is that very few of these innovations are currently being reimbursed by insurers.

Interestingly, some health policy experts and clinicians believe the lack of reimbursement isn't necessarily a pressing issue. “With AI, so much of the conversation is about how do we get paid for the individual technology,” says Ateev Mehrotra, chair of health services, policy, and practice at the Brown University School of Public Health. He advocates for a shift in focus: “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?’”

Conversely, industry stakeholders are sounding alarms over the potential impact of insufficient reimbursement. They argue that without a clear payment structure, the adoption of beneficial AI technologies could be severely hindered, preventing them from reaching patients who could benefit. By 2026, as more devices enter the market, the debate over payment methodologies is expected to intensify. Currently, only three AI devices have received a permanent CPT code from the American Medical Association (AMA), a crucial step for securing reimbursement from Medicare and, consequently, most private insurers. Meanwhile, over 20 temporary category III codes have been issued to various AI devices, many of which are anticipated to become permanent.

With this looming influx of technology, the AMA is exploring a new coding category specifically for AI products that may not require physician input. This reflects a broader industry push to develop reimbursement models that are more favorable to physicians. At the same time, the Centers for Medicare and Medicaid Services (CMS) are grappling with their own payment frameworks for AI, primarily relying on vendors' valuations of their software. Legislative proposals have even emerged to formalize payment pathways for AI technologies.

These discussions on payment structures are not just theoretical; they are urgent. As Pelu Tran, CEO of AI governance company Ferrum Health, states, “Health systems are looking at AI and wondering how the heck they pay for this.” Specific case studies illustrate how payments might function across various fields in 2026.

AI in Coronary Plaque Analysis: A Case Study

This coming January, Medicare will begin reimbursing physicians over $1,000 for using AI to analyze the type and quantity of plaque in a patient’s coronary arteries. This technology holds the distinction of being one of only three AI tools that has received a Category I CPT code from the AMA, making its reimbursement patterns a critical data point for ongoing discussions about payment strategies. By contrast, an earlier technology that also employs AI to assess blood flow through coronary arteries, also commands a payment of over $1,000 from Medicare. “We’re just now at a place where we’re seeing close to universal payments for the procedure,” notes Eric Rubin, primary CPT adviser for the American College of Radiology, during a recent session at the Radiological Society of North America meeting in Chicago.

However, payments for plaque analysis have been inconsistent. Rubin admits that while reimbursement is slowly becoming more uniform, it remains a work in progress. A crucial factor in this development will be whether physicians can accurately identify which patients would benefit from this technology and document their conditions effectively to qualify for payment.

For instance, many doctors may not realize the necessity of ordering a CT scan that includes AI-assisted analysis. As Jacob Agris, vice president of product management at ConcertAI, explains, their new product aims to streamline this process by flagging appropriate cases for physicians.

Self-Pay Models: The Case of Breast Imaging

In the realm of breast imaging, the scenario is markedly different. Women seeking their annual mammograms can opt for an AI add-on to identify suspicious lesions, but without insurance reimbursement, it often falls to the patients to cover the cost—typically between $40 and $50. “All of us physicians felt like moving to a self-pay model was not our preferred approach,” admits Greg Sorensen, chief science officer at RadNet, a large outpatient imaging provider. Despite this, RadNet’s AI screening program has generated profits, yielding approximately $30 million in revenue from roughly half of the 1.6 million mammograms they conduct annually.

As the trend towards self-pay continues, newer algorithms are emerging. Some radiology centers now offer AI add-ons that analyze breast arterial calcification at around $90. Additionally, an AI device called Clairity Breast, which predicts a patient's five-year risk of developing breast cancer, is set to launch a pilot program at Beth Israel Deaconess Medical Center at a price point of $199. However, one notable exception includes an AI-based interpretation of breast ultrasound, which has been assigned a temporary CPT code.

As out-of-pocket expenses for women increase, pressure is mounting on insurers to cover certain AI applications in breast imaging. “For reimbursement, we really do need CPT codes for these AI products because otherwise we’re going to be allowing or enabling patients who can afford it to get the AI, which has improved performance,” asserts Sarah Friedewald, academic division chief of breast imaging at Mass General Brigham.

This increasing financial burden for women, who typically undergo annual mammograms, could prompt insurers to reconsider their stance on AI reimbursement.

In conclusion, while the path to navigating reimbursement for clinical AI technologies is fraught with challenges, it also presents opportunities for the healthcare landscape. As various stakeholders—from health systems to patients—adapt to these changes, the implications for care delivery, patient outcomes, and healthcare costs will be significant. The future of AI in healthcare is not just about technological advancement; it's about ensuring that these innovations translate into tangible benefits for all involved.