The Most Dangerous Thing in Healthcare Isn’t AI. It’s What’s Underneath It
TL;DR
Deterministic, explainable AI tooling, cryptographic methods, and decentralized solutions are already in production in regulated life sciences and interoperate with hospitals’ existing cloud infrastructure, but agreement on what ‘verified’ means is still missing: a shared, open, independent standard.
Abstract
Hospitals and insurers face financial pressure to use artificial intelligence (AI) but are deploying it on top of older systems that cannot show what the AI actually did. Documented cases show algorithms denying care at very high error rates with almost no outside check. Autonomous AI agents make this worse, because they can act differently from one run to the next. Deterministic, explainable AI tools and compliance-ready cloud platforms already exist for regulated healthcare, and they work with the existing hospitals’ infrastructure. What is still missing is agreement on what ‘verified’ means: a shared, open, independent standard, not a vendor’s word that its own system is safe. Key issues:• Financial strain is pushing adoption of AI, and oversight has not kept pace.• Documented AI-assisted denials show high reversal rates on appeal, alongside very low appeal rates.• Non-deterministic AI agents cannot be certified once; they require continuous verification.• Deterministic, explainable AI tooling, cryptographic methods, and decentralized solutions are already in production in regulated life sciences and interoperate with hospitals’ existing cloud infrastructure.• The remaining gap is a shared, open, independent standard that defines ‘verified’ because verification cannot belong to the party being verified.