JMIR Publications

ABSTRACT

Background:

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 like 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 pre-registered four-group randomized survey experiment among a national sample representative of the US population on several demographic benchmarks (n = 1,762). Participants viewed identical doctor profiles, with varying AI usage descriptions: no AI mention (control, n = 421), explicit non-use (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 socio-demographic 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 (Mean Control group = 0.50; Mean No AI group = 0.63) and lowest when AI was extensively used (Mean Extensive AI group = 0.30, Mean Moderate AI group = 0.34). A linear regression controlling for demographics suggested that the negative effect of AI assistance was significant with a large effect size (β = -0.45, 95%CI [-0.49, -0.40], t = -20.81, P < .001). This pattern was consistent for trust in the doctor as a person (β = -0.33, 95%CI [-0.37, -0.28], t = -14.41, P < .001) and as a professional (β = -0.40, 95%CI [-0.45, -0.36] t = -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 = 0.09, 95%CI [0.04, 0.13], t = 4.06, P < .001) but expanded as self-reported knowledge increased (interaction term: β = -0.04, 95%CI [-0.08, 0.00], t = -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 healthcare. Clinical Trial: https://osf.io/v8kzs/ (OSF Pre-registration)