Effects of Type and Timing of Clinician-Facing AI Support on Patient Trust in Medical Consultations: 2 Vignette Experiments

Abstract Background AI-based clinical decision support systems are increasingly integrated into medical practice, creating hybrid decision-making processes in which physicians and AI systems jointly contribute to clinical judgments. Yet, how different forms of such AI support affect patients’ trust in hybrid medical decisions remains poorly understood. Objective This study aimed to examine how the type and the timing of physician AI support influence potential patients’ trust in the medical decisions, perceptions of the hybrid decision-making process, and intentions to follow the medical advice. Methods In 2 preregistered vignette-based online experiments, 489 (study 1) and 570 (study 2) members of the general public in Germany imagined 4 medical consultations, in which the physician used no AI support, descriptive AI support (informational or visual assistance), or diagnostic AI support (preliminary diagnostic suggestions). Study 2 additionally manipulated the timing of AI support, namely, whether the physician reviewed AI advice after having made an independent own assessment (sequential decision-making) or not (concurrent decision-making). Participants rated their trust in the medical decisions, trustworthiness of the medical provider, uniqueness neglect, and willingness to follow the medical advice on 7-point Likert scales, with greater values representing stronger agreement. Linear mixed-effects models were used for quantitative analyses. Open-ended responses (N=2607) were analyzed qualitatively to identify recurring themes regarding trust in AI-supported decisions. Results In study 1, the physician’s use of diagnostic AI support compared with descriptive AI support produced significantly lower mean ratings of trust in the medical decisions (5.00 vs 5.37; t 486 =3.51; P= .002) and perceived provider trustworthiness (4.93 vs 5.40; t 486 =4.46; P< .001), as well as higher mean ratings of perceived uniqueness neglect (3.24 vs 2.93; t 486 =2.72; P= .02), but no significant differences regarding the willingness to follow the advice (5.42 vs 5.62; t 486 =1.74; P= .25). Study 2 replicated these results and revealed a significant interaction effect between type and timing of AI support for trust in the medical decisions ( t 436 =2.71; P= .007), perceived provider trustworthiness ( t 436 =2.78; P= .006), and perceived uniqueness neglect ( t 436 =−2.34; P= .02) but not for willingness to follow the advice ( t 436 =1.81; P= .07). Specifically, diagnostic AI support was evaluated less favorably than descriptive AI support when the physician reviewed primary medical information and AI output simultaneously but not when the physician first assessed the primary medical information independently before reviewing the AI output. Qualitative responses showed that participants were concerned that erroneous AI output biases physicians’ judgments and indicated that physician independence could strengthen trust. Conclusions The use of AI to support physicians appears more acceptable when used for analytical rather than decisional support, or when physicians’ decisional independence is visibly retained, suggesting that trust in AI-supported medical decisions depends not only on whether physicians use AI support but also on subjective perceptions of how it is used.