Why AI Radiologists Are Still Stuck in Beta

The AI That Wasn’t Ready for the X-Ray

For years, artificial intelligence has been hailed as the future of medical imaging—poised to transform radiology by detecting diseases faster and with more consistency than human doctors. Yet in practice, the technology remains largely on the sidelines of healthcare. Although over 500 AI imaging tools have received FDA clearance, few have become a permanent fixture in clinical workflows. Many are only being tested or used under limited conditions, according to hospital staff and technology experts. High hopes have encountered real-world complexity: integrating these tools into legacy infrastructure is complicated, doctors are wary of relying on AI for life-or-death diagnoses, and in some cases, the AI fails to perform under diverse clinical conditions.

Too Many Tools, Not Enough Trust

One reason for the slow adoption is the fragmented nature of the AI radiology market. Startups and tech giants alike have poured resources into building narrowly focused tools—each designed to detect a single type of anomaly, such as lung nodules or brain bleeds. The result is a complex patchwork of apps that don’t always play well together or with hospital IT systems. Moreover, many of these tools were trained on limited datasets, making their performance uneven in real-world settings. Radiologists and health systems remain skeptical, particularly when early results show AI assistance can sometimes increase workloads or produce false positives that erode trust rather than build it.

Regulatory Greenlights, But Red Lights at the Hospital

Despite receiving FDA clearance, most AI tools don’t go through rigorous validation in hospital environments before deployment. That disconnect—between regulatory approval and clinical utility—has bred caution among practitioners, who often see