Social signals dominate clinician trust in medical AI
Accuracy and outcome data are often treated as the main determinants of trust in automation. We test this assumption against an account in which clinicians evaluate artificial intelligence (AI) using social-cognitive cues. We introduce SPOT (Synergistic Process-Outcome Trust), a Bayesian framework that models trust as inference over latent agent properties from outcome and process signals. In a within-subjects vignette study, 275 Spanish healthcare professionals evaluated 16 clinical AI scenarios varying three outcome signals (accuracy, intervention timing, prognosis) and five process signals (interface familiarity, explainability, team consensus, supervision, personalization). Ratings were analysed with linear mixed models and participant-wise cross-validation. Process signals dominated trust formation. Interface familiarity (beta = 0.71), team consensus (beta = 0.64), and explainability (beta = 0.63) produced large effects, with p values below .001, whereas stated accuracy produced a small effect (beta = 0.10). Process signals accounted for 80.3 percent of the total absolute effect magnitude and outcome signals for 19.7 percent. Trust baselines were highly idiosyncratic and poorly recovered from demographics (R-squared = 0.06 under group-wise cross-validation). Clinicians appear to treat AI systems as agents whose trustworthiness requires social validation as well as technical verification. Building trustworthy clinical AI therefore involves social, institutional, and technical factors.