Cardiology Fights Back: AI Prior Auth Denials Threaten Heart Care

AI was supposed to bring efficiency to healthcare, but its use in administrative tasks like prior authorization is just adding friction and, frankly, putting patients in harm’s way. While the American Medical Association (AMA) has raised general alarms, the pushback from cardiology specialty societies is much more pointed, a focused resistance against AI-driven denials that are blocking access to tests and procedures we need to save lives. This goes way beyond administrative burnout. It strikes at the core of clinical integrity in cardiac care.

Cardiology’s Specific Stand Against Algorithmic Denials

The American Society of Nuclear Cardiology (ASNC), a major cardiac imaging society, is taking a hard line against the unchecked use of AI in prior authorization. Their focused action shows just how vulnerable cardiology is to these algorithmic decisions, especially when it comes to advanced diagnostic imaging and interventions. This isn’t just ASNC echoing the AMA’s broader complaints. It’s a targeted move driven by the time-sensitive nature of cardiac care. Dr. Suman Tandon, a cardiac imaging specialist, formally represented ASNC to back Resolution 226 at the AMA House of Delegates, a resolution demanding that the AMA push for regulation of these algorithm-based reviews and for federal checks on AI claims systems for bias. It’s a clear acknowledgment that AI, left on its own, can create systemic biases that hurt certain patients or disrupt established clinical pathways. The fear isn’t just about inconvenience, but about algorithmic drift causing the AI to misread cardiac data or guidelines, leading to denials for tests that are absolutely necessary. On top of that, ASNC is backing H.R. 3514, a bipartisan bill that’s already moving through House committees. The bill is designed to force some transparency and accountability onto Medicare Advantage plans that use AI for prior authorization. It would require these plans to report their case counts, denial rates, and the reasons for those denials directly to the Centers for Medicare & Medicaid Services (CMS), including (and this is the important part) the impact on vulnerable populations Congressional record for H.R. 3514. This kind of reporting is the only way to spot patterns of unfair denials and hold insurers accountable for what their AI systems are doing to patients. Right now, the lack of transparency gives insurers a data moat, making it impossible for doctors or regulators to see the real-world impact of these algorithms.

The Broader Context: AI Claims Systems and Regulatory Gaps

The activism from cardiologists like Dr. Tandon and groups like ASNC shows that we’re all starting to get that AI in claims processing isn’t some neutral tool. It’s not like a SaMD (Software as a Medical Device) that helps with a diagnosis. This AI is a gatekeeper, and letting it run without supervision can have serious clinical consequences. The main problem is the total lack of federal oversight and the fact that these administrative AI systems aren’t held to GMLP (Good Machine Learning Practice) principles. Think about it: the diagnostic AI we use in the clinic has to go through rigorous FDA pathways, 510(k) clearance, De Novo classification, sometimes even Breakthrough Device Designation for new cardiac tech. Yet the AI an insurer uses to deny that care operates in a regulatory vacuum. There’s no equivalent to a PCCP (Predetermined Change Control Plan) for these algorithms, which means an insurer can change how it works overnight without any external review, leading to algorithmic drift that directly affects patient care. This double standard is a huge problem for clinicians. We’re held to incredibly strict standards of care, but we’re getting denials from opaque, unregulated AI. The secrecy extends right down to the AI’s design. How can you evaluate an algorithm for biases that might cause it to undertriage a cardiac emergency if you have no idea what training data, validation metrics, or decision logic it’s using? In a field like cardiology, where every minute counts for diagnoses and interventions, that’s a terrifying question.

Impact on Cardiac Diagnostic Tests and Procedures

The fight from ASNC is driven by the direct harm these denials cause to cardiac care. We rely on advanced cardiac imaging, things like nuclear stress tests, cardiac CT, and cardiac MRI, to diagnose complex cardiovascular conditions and map out treatment plans. These tests give us a ton of information, but they can be expensive, which makes them a perfect target for an insurer’s AI looking to cut costs. When an AI system denies a medically necessary cardiac test without any clinical context or human review, it’s not just an administrative hiccup. It creates delays in diagnosis and treatment for conditions like coronary artery disease or heart failure, and those delays can lead to disease progression, worse outcomes, and even death. The real worry is that these systems, built for raw efficiency, simply don’t have the nuance to understand a complex clinical presentation or weigh individual patient risk factors, so they spit out a one-size-fits-all denial that is completely wrong for the patient in front of you. This is precisely why ASNC supports H.R. 3514. Forcing insurers to report their denial rates and reasons would finally give us the hard data needed to see if AI is disproportionately blocking specific cardiac tests or harming certain groups of patients. Cardiovascular Business article on ASNC’s advocacy

The Need for Clinically Validated AI in Administrative Functions

The cardiology community’s stance makes a simple point: any AI used in healthcare has to be clinically validated and overseen, whether it’s for clinical work or just administration. The current setup, where opaque algorithms or even simple chatbot-like systems can deny access to care without the kind of scrutiny we apply to SaMDs, is completely unsustainable. Responsible AI in this area would have to be transparent, auditable, and proven to be free from bias. That means using models trained on diverse and representative data, with clear pathways for a human doctor to override a denial and appeal it. It also means having a real QMS (Quality Management System) for the AI itself, so its performance is constantly monitored for drift and any changes are managed properly. The advocacy from groups like ASNC and specialists like Dr. Tandon shows that physicians are ready to challenge the status quo when new technology threatens our patients. Their work is a reminder that while AI has a lot of potential, its use has to be guided by clinical need and patient safety, not just by an insurer’s bottom line. The push for federal oversight and transparency in AI prior authorization is a fight to protect medical judgment and, in the end, our patients.