Anyone who has had to request prior authorization from a health insurer for a physician-prescribed treatment knows the frustration: forms, waiting, silence. The process, designed as a check against waste, has become a labyrinth that drives many patients to abandon recommended therapies before learning whether they will be covered. Artificial intelligence now promises to speed things up. But that promise clashes with a sobering statistic: 61% of American physicians, surveyed by the American Medical Association in 2025, believe AI will worsen unjustified denials, not reduce them.

The logic seems straightforward: an algorithm can scan thousands of requests, cross-reference clinical guidelines and records, and approve in seconds what a human reviewer would take days to validate. Yet the mechanism hides a trap. AI is not neutral: it is trained on historical data and on parameters set by insurers—most often to contain costs. If the model learns that certain procedures are “high cost” and associates them with a higher denial rate, a vicious cycle self-perpetuates. The algorithm’s speed is no virtue if it systematically multiplies errors that are difficult to challenge.

Here a broader issue emerges, one that goes beyond the medical debate: data sovereignty and model transparency. AI-based authorization platforms often run in the cloud, on the infrastructure of major tech companies, far from the control of hospitals and patients. When a request is denied, understanding exactly why the AI made that decision becomes an uphill struggle. Internal mechanisms remain opaque, and with them vanishes any chance of meaningful appeal. In a sector where health data is among the most protected in the world, delegating life‑altering decisions to an external system is a choice that touches privacy, equity, and legal accountability.

For those designing similar systems, the lesson is clear: deploying AI for sensitive decisions cannot be reduced to a simple cloud API integration. On‑premise or hybrid architectures, where processing takes place on machines under the direct control of the insurer or a regulatory body, could offer a way out. They would enable full model audits, inference logs, fairness checks, and granular control over which data feeds the fine‑tuning. This is not merely about operational efficiency; it is the ridge that separates a challengeable system from a black box that decides people’s health.

The hostility of physicians is not a rejection of technology, but an alarm bell about the direction being taken. If the automation of prior authorization continues down the path of profit maximization without transparency, distrust will grow, ultimately suffocating even the potential benefits. At stake for the insurance industry, and for those providing the tools, is the very credibility of an AI that promises to cure bureaucracy but risks aggravating the system’s disease. The open question remains: will we invest in infrastructures that restore control and responsibility, or will we take the shortcut of an algorithm that denies in silence?