Transparency features alone are insufficient for trust calibration in artificial intelligence diagnostic support

Clinician trust in artificial intelligence (AI) is a frequently cited barrier to adoption, yet there is limited empirical evidence on how transparency features influence trust. This study aimed to address this evidence gap.

We conducted a randomized controlled experiment in which 220 clinicians were randomized to 1 of 3 conditions: no transparency (control), numeric confidence scores, or narrative explanations with citations, using simulated AI diagnostic assistance across clinical case vignettes.

Transparency did not influence tool-specific trust [F(2, 214) = 0.38, P = .68], global trust [F(2, 217) = 0.13, P = .88], or perceived information credibility [F(2, 210) = 0.42, P = .66]. Confidence increased following any AI assistance (+6.7%), but narrative explanations were associated with greater change in confidence compared with both control (P = .024) and numeric confidence (P = .0051). Participants changed or refined diagnoses after reviewing AI output 45.8% (SD 23.4%) of the time, with participants in the control group demonstrating higher levels of behavioral influence than transparency conditions (P = .0006).

Trust, confidence, and behavioral influence represent distinct constructs and are impacted differently by transparency methods. The difference in these contributions to clinician-AI partnerships should be considered when designing for safe trust calibration, with particular consideration for the dissociation between trust and confidence.

These findings suggest common interface transparency features alone failed to produce a detectable, meaningful shift in clinician trust in AI assistance and that changes may instead alter behavioral influence and confidence without a corresponding change in trust.