AI in radiology is failing. Not because the technology is flawed, but because it refuses to integrate with radiology practices.
We have AI that flags fractures, detects lung nodules, and triages critical findings in seconds. Yet most radiologists still ignore it. Why? The tools don’t talk to the systems we actually use.
Dictation software remains the biggest bottleneck. AI findings sit in a separate viewer while we dictate in our usual platforms. No auto-population. No seamless insert. Extra clicks kill productivity.
Worklists are even worse. AI prioritization lives in its own silo. We open the PACS worklist, then a separate AI dashboard, then decide what to read. The promised “smart queue” never arrives.
PACS vendors claim integration, but it’s usually a bolted-on viewer or a clunky API that requires IT projects and months of validation. Most sites still launch AI in a parallel tab, if at all.
Analytics dashboards? They exist, but only for the AI vendor’s metrics. No unified view showing how AI actually moves RVUs, reduces turnaround time, or affects report accuracy across the enterprise.
The result is predictable: low adoption, frustrated radiologists, and executives questioning ROI. Billions invested, yet AI remains a science project in most departments.
True success requires native, bidirectional integration. AI that pushes structured findings directly into the report, auto-updates the worklist in real time, lives inside the PACS, and feeds a single enterprise analytics platform.
The technology is ready. The integration isn’t.
At Shadowfax, we see this every day. The industry is overdue for change.
A few very exciting product updates are coming soon…