Five Reasons Why Radiology AI Has a Trust Problem
Radiologists enter the profession for a simple, noble purpose: to help people get better. Their expertise comes from years of training in anatomy, physiology and the subtle imaging patterns that differentiate normal from life-threatening.
The job, on paper, is straightforward. The lived reality is not. Today’s radiologist works under the strain of:
- Worklists with hundreds of scans waiting to be read
- Interruptions that fracture deep diagnostic thinking
- Incomplete or inaccurate clinical histories
- Medicolegal scrutiny that turns dictation into a potential liability
- Incidental findings and closing the communication loop
- Speech recognition errors that chip away time
This environment shapes how radiologists experience new technology — including AI. Every AI claim is evaluated against this pressure, which is exactly where the trust problem begins.
66% of radiologists are concerned about personal liability when AI errors occur. - 2025 Future Health Index
1. AI Arrives in a System Already at Its Breaking Point
AI is often framed as the cure for radiology's operational crisis. But when clinicians are overloaded, “help” that isn’t actually helpful becomes another source of cognitive burden.
Radiologists don’t distrust AI because they dislike innovation, they distrust AI that overpromises, underdelivers and disrupts workflows at the wrong moment.
Radiology burnout rates are estimated between 37.4% - 46% - Jing, A.B., Garg, N., Zhang, J. et al., npj Health Systems, 2025
2. A History of Data Problems Has Made Clinicians Skeptical
Radiologists know that AI learns from historical data, and they know that data can be biased, inconsistent or non-representative. So when they hear accuracy claims, the immediate question becomes: “Does this performance hold for our scanners, our workflows and our patients?” Their skepticism is not resistance — it’s professional responsibility.
“We recognise that there are significant challenges related to the wider adoption and deployment of AI into healthcare systems. These challenges include, but are not limited to, data quality and access…” - Bajwa J, Munir U, Nori A, Williams B., Future Healthcare Journal, 2021
3. The “Black Box” Problem
Radiologists must make defensible, explainable decisions. Any tool that can’t explain why it made a suggestion is fundamentally misaligned with clinical accountability. In radiology — a specialty defined by pattern recognition — the why is as important as the what.
56% of physicians said model transparency was the most important trust factor for AI. - Gotta J, Grünewald LD, Koch V, et al., Insights Imaging, 2025
4. AI Mistakes Erode Trust Faster Than AI Successes Build It
AI can be correct 1,000 times, but one error in a high-stakes workflow can reset trust to zero. The risk isn’t just that AI can be wrong, it’s that clinicians may trust it too quickly. One study showed that when AI draws a box around an abnormality, physicians tend to follow its conclusion even when it’s incorrect.
That’s why radiologists need partners who help safeguard against overreliance and understand that trust must be actively managed, not assumed.
"When we rely too much on whatever the computer tells us, that's a problem, because AI is not always right.” - Paul H. Yi, MD, Director of Intelligent Imaging Informatics at St. Jude Children's Research Hospital, via RSNA
5. Governance Gaps Amplify Distrust
AI won’t transform healthcare on technology alone. The real determinant of trust is governance. When health systems lack clear strategy, accountable roles and disciplined oversight, even good models create confusion, risk and clinician skepticism.
80% of health systems have no or limited governance process in place for AI use despite most having a pilot in the works. - Healthcare Financial Management Association, 2025
How Do Healthcare Stakeholders Build Trust in Radiology AI?
Radiology’s AI trust gap is real, but it is not immovable. When health systems define how AI is assessed, validated, implemented and monitored, trust goes from abstract to operational. If you're ready to start addressing the radiology AI trust problem, this practical, five-step governance framework can help get you started.
