Three out of four organizations using imaging AI cannot demonstrate a quantified positive return. The mistake may begin with what the CIO in radiology buys first.
Radiology has spent years asking whether AI works. It now has a harder question: who can prove it pays?
In Black Book Research‘s December report, based on responses from 212 imaging leaders, only 24% of organizations using AI could report a quantified positive financial return. The rest might have benefited. They couldn’t show it.
- The temptation is to respond with longer pilots, more committees, and another signature. But an additional approval gate cannot repair a purchase whose financial benefit was never aligned with the buyer.
Consider detection and triage. The algorithm can be clinically valuable, even lifesaving.
- But a faster stroke response may save resources in neurology or emergency care while radiology pays for deployment and integration. That is a real benefit with a misplaced ledger entry. Clinical value and departmental ROI should not be confused.
The first MRI AI investment should pass two tests. Can it generate measurable improvement in the imaging operation? And can it work across the entire fleet – from aging magnets to newer scanners already using OEM AI?
Vendor-neutral image enhancement offers one answer. By improving accelerated scans across makes and generations, it can create usable capacity without buying a new scanner.
- But that capacity only becomes revenue if schedules, staffing, and referral volumes let departments fill new appointments. Count completed exams, overtime, wait times, and avoided capital costs – not theoretical minutes saved.
At 3T Radiology & Research, exam volume rose 42% in the first two months after deployment of SwiftMR from AIRS Medical, generating $205,567 in reported incremental revenue.
SimonMed took that logic enterprise-wide: over 200 MRI scanners, different manufacturers, generations and field strengths, deployed in 10 days.
- The objective wasn’t to improve one scanner. It was to bring an entire mixed fleet under a common performance strategy.
That decision also creates a foundation for what comes next. An image is the input for every clinical AI tool, whether it detects, quantifies, or segments.
- If scanner-to-scanner differences affect noise, contrast, and sharpness, they can also affect those outputs and the longitudinal comparisons built on them.
Establishing more consistent images across the network gives those tools a stronger starting point, with reproducibility validated rather than assumed.
The Takeaway
Radiology doesn’t need to abandon diagnostic AI. It needs a purchasing sequence tied to who benefits, what can scale, and how outcomes are audited. Start with the MRI infrastructure you own. Measure the operational return. Then build the intelligence.
Jina Park is Chief Strategy Officer at AIRS Medical, maker of SwiftMR, an FDA-cleared, vendor-neutral MRI image-enhancement solution compatible with scanners from multiple manufacturers at field strengths up to 3T.
Source: Black Book Research, RSNA Imaging AI Flash Poll, December 4, 2025.
