Medical AI has a proof problem
Medical AI's lab wins aren't translating to patient outcomes. Here's why deployment is stalling.
Why it matters
Despite breakthrough benchmarks and FDA approvals, medical AI has failed to demonstrably improve real-world clinical outcomes or practice adoption at scale — a critical gap between capability and deployment that practitioners and regulators are now forcing into the open.
The key facts
5 to knowMedical AI advances (model capability, benchmark performance) not translating to measurable improvements in patient care
Gap between laboratory validation and real-world clinical effectiveness
Deployment and adoption barriers in healthcare institutions
Regulatory approval not tied to actual outcome improvement
Industry facing scrutiny on 'proof problem' — capability claims vs. evidence of clinical impact
Go to the source
Financial Times Technologyft.com
Publisher excerpt: The technology’s advances have not yet translated into big improvements in real-life care