Do Radiologists Trust AI Diagnostic Suggestions?

A Spectrum of Attitudes, Not a Single Verdict

There isn’t a single, uniform answer to how radiologists feel about AI diagnostic suggestions — attitudes across the profession span a genuine spectrum, from radiologists who have integrated specific AI tools enthusiastically into their daily workflow to others who remain more cautious or skeptical, particularly about tools that haven’t been thoroughly validated for their specific practice setting. This variation reflects the reality that “AI in radiology” isn’t one single technology with one track record; it’s a collection of many different tools, each with its own evidence base, and radiologists’ trust tends to track closely with their direct experience and knowledge of a specific tool’s actual reliability rather than a general attitude toward AI as a broad concept.

What Drives Trust in Practice

Several factors consistently show up in discussions of what builds or undermines radiologist trust in a specific AI tool. Transparent, accessible evidence about how a tool performed in validation studies — ideally studies relevant to the radiologist’s own patient population and equipment — tends to support greater confidence. Radiologists have also expressed a preference for tools that offer some degree of interpretability, meaning the tool’s output isn’t a pure “black box” verdict but comes with some indication of what pattern led to the suggestion, which can help a radiologist weigh the suggestion against their own independent assessment rather than simply accepting or rejecting it wholesale.

Direct, accumulated experience with a specific tool over time also plays a significant role. A radiologist who has used a particular AI tool extensively and has seen firsthand where it tends to perform reliably, and where it has produced false positives or missed findings, develops a more calibrated sense of how much weight to give its suggestions in different situations — a kind of practical, experience-based trust calibration that generic performance statistics alone don’t fully provide.

Legitimate Concerns Coexist With Genuine Openness

Alongside general openness to useful tools, radiologists and their professional organizations have also raised legitimate concerns worth taking seriously: the risk of over-reliance leading to complacency in independent review, the possibility that AI suggestions could introduce new types of errors distinct from traditional human error patterns, and the importance of ensuring a tool’s validation data actually reflects the population and equipment where it’s being deployed. These concerns have generally translated into professional guidance emphasizing that AI should function as a support to, not a substitute for, a radiologist’s own independent clinical judgment and ultimate responsibility for interpretation.

Bottom Line

Radiologist trust in AI diagnostic suggestions varies considerably and tends to be shaped by a specific tool’s validated performance, transparency, and a radiologist’s own direct experience with it, rather than reflecting any single, profession-wide verdict — with most guidance emphasizing AI as a support tool rather than a replacement for clinical judgment.

Go deeper

• What Factors Most Influence Whether People Trust an AI System?