The evidence base for AI in radiology has matured. There are now stronger signals for specific detection tasks, but the operational value is often less glamorous: faster reporting, less repetitive work, and fewer avoidable context switches.
What is well established.
- AI helps in narrow tasks such as fractures, nodules, hemorrhage, and mammography support.
- Workflow integration explains adoption better than small AUC differences.
- Domain-specific models outperform generic models where language and context matter.
- Human review remains essential for safety and accountability.
The real gain today is cognitive load, not replacing the radiologist.