ROI of AI in radiology: productivity, time, fewer errors
What diagnostic-support AI actually changes for productivity, turnaround and errors, backed by sourced figures and studies.
Medically reviewed by Dr Alexandre Parpaleix, MD-PhD, CEO and co-founder of Milvue
What productivity gain can radiology expect from AI?
Start from a sourced figure, never a promise. For adult fracture, the radiologist's sensitivity rises from 0.81 to 0.98 once assisted (FDA reader study). On measurements, automation removes a manual tracing clocked at 203 seconds per patient on weight-bearing feet (AI/MDPI, 2025), now near-instant. At constant volume, the time freed shifts to reading and availability. Model your own case with the ROI calculator.
How many more exams can be read per session?
Up to +64% more exams read per session on an image-to-report flow, when TechCare Report is integrated in the RIS/PACS (figure reported by a partner radiology center). The assistant pre-fills the report with measurements (AI and non-AI) and offers voice dictation in 17 languages. It is intelligent radiology dictation, subscription software, not a medical device: it speeds up writing without replacing the radiologist, who stays in charge of every line of the report.
Does AI genuinely cut errors and delays in the emergency department?
Yes, provided each figure cites the right source. In the ED (European Journal of Radiology Open, 2023, 1,772 patients), the AI reaches an overall AUC of 0.954 and catches 90% of the cases an emergency physician had misread. The hour of waiting saved at Aalborg University Hospital is a distinct result, tied to prioritization. Conflating the two would weaken the case; keeping them separate makes it defensible.
Will a model that passes a radiology exam cut costs in half?
That is the fashionable 2026 promise: models that draft the report, some claiming to pass a certification exam. Passing an exam is a benchmark; working a shift is another thing entirely. ROI does not come from a model's score, but from the time genuinely returned to the radiologist: 40 million analyses a year, 1M+ TechCare Report exams already produced. We do not see one more competitor; we see a conviction confirmed: AI should be measured in routine use, not on a test set. The detail of this shift is in our VLM piece.
How do you actually calculate the return on investment?
Start from your real volumes and apply the sourced gains: adult fracture sensitivity from 0.81 to 0.98 (FDA reader study), up to +64% more exams read per session (partner center, integrated RIS/PACS), 90% of misread ED cases caught (EJRO 2023). Weigh the time freed against a subscription cost, then add indirect benefits (fewer repeat exams, shorter waits). With 600+ sites and 40M analyses/year, the orders of magnitude are field-proven. The real question is not what AI costs, but what you do with the time it gives back: Our AI does not replace radiologists. It makes them irreplaceable. The ROI calculator structures this calculation.
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