WorkSeptember 18, 2026via Financial Times Technology
Medical AI has a proof problem
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.
Key signals
- Medical 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
The hook
Medical AI's lab wins aren't translating to patient outcomes. Here's why deployment is stalling.
The technology’s advances have not yet translated into big improvements in real-life care