Impact of AI-Assisted Diagnosis on American Patients' Trust in and Intention to Seek Help From Health Care Professionals: Randomized, Web-Based Survey ...

Abstract

Background: Artificial intelligence (AI) technologies are increasingly integrated into medical practice, with AI-assisted diagnosis showing promise. However, patient acceptance of AI-assisted diagnosis, compared with human-only procedures, remains understudied, especially in the wake of generative AI advancements such as ChatGPT. Objective: This research examines patient preferences for doctors using AI assistance versus those relying solely on human expertise. It also studies demographic, social, and experiential factors influencing these preferences. Methods: We conducted a preregistered 4-group randomized survey experiment among a national sample representative of the US population on several demographic benchmarks (n=1762). Participants viewed identical doctor profiles, with varying AI usage descriptions: no AI mention (control, n=421), explicit nonuse (No AI, n=435), moderate use (Moderate AI, n=481), and extensive use (Extensive AI, n=425). Respondents reported their tendency to seek help, trust in the doctor as a person and a professional, knowledge of AI, frequency of using AI in their daily lives, demographics, and partisan identification. We analyzed the results with ordinary least squares regression (controlling for sociodemographic factors), mediation analysis, and moderation analysis. We also explored the moderating effect of past AI experiences on the tendency to seek help and trust in the doctor. Results: Mentioning that the doctor uses AI to assist in diagnosis consistently decreased trust and intention to seek help. Trust and intention to seek help (measured with a 5-point Likert scale and coded as 0‐1 with equal intervals in between) were highest when AI was explicitly absent (control group: mean 0.50; No AI group: mean 0.63) and lowest when AI was extensively used (Extensive AI group: mean 0.30; Moderate AI group: mean 0.34). A linear regression controlling for demographics suggested that the negative effect of AI assistance was significant with a large effect size (β=−.45, 95% CI −0.49 to −0.40, *t* ~1740~=−20.81; *P* <.001). This pattern was consistent for trust in the doctor as a person (β=−.33, 95% CI −0.37 to −0.28, *t* ~1733~=−14.41; *P* <.001) and as a professional (β=−.40, 95% CI −0.45 to −0.36, *t* ~1735~=−18.54; *P* <.001). Results were consistent across age, gender, education, and partisanship, indicating a broad aversion to AI-assisted diagnosis. Moderation analyses suggested that the "AI trust gap" shrank as AI use frequency increased (interaction term: β=.09, 95% CI 0.04-0.13, *t* ~1735~=4.06; *P* <.001) but expanded as self-reported knowledge increased (interaction term: β=−.04, 95% CI −0.08 to 0.00, *t* ~1736~=−1.75; *P* =.08). Conclusions: Despite AI's growing role in medicine, patients still prefer human-only expertise, regardless of partisanship and demographics, underscoring the need for strategies to build trust in AI technologies in health care. Trial Registration: OSF Registries osf.io/5vcdg; <https://osf.io/5vcdg>