Accepted for/Published in: JMIR Formative Research
Date Submitted: Apr 15, 2026
Date Accepted: Jul 31, 2026
Factors Associated with Professionals’ Context-Specific Trust in AI-Based Screening for Autism Spectrum Disorder: Cross-sectional survey study
ABSTRACT
Background:
In medicine, trust in Artificial Intelligence (AI) is associated with individual factors and is particularly relevant in autism spectrum disorder (ASD) screening, where diagnostic criteria are complex, heterogeneous, and inherently uncertain. Although some AI systems demonstrate high sensitivity, clinicians are not always convinced of their utility.
Objective:
This study examined individual factors associated with professionals’ trust in AI-based ASD screening.
Methods:
Fifty-five professionals in ASD assessment participated. We used an AI-based screening method based on a geometric shape drawing task. Expert clinicians selected two ambiguous screening scenarios. Participants made independent judgments while considering AI output and rated trust in each decision on a 7-point Likert scale.
Results:
A generalized linear mixed model analysis showed significant between-participant (ID) variability and a small but significant trial effect. Individual differences accounted for a substantial proportion of variability in trust. Years of professional experience (t(43.6) = −2.21, P = .032) and length of education required to obtain professional credentials (t(40.3) = −2.46, P = .018) had significant negative effects. General trust in AI had a significant positive effect (t(43.5) = 2.83, P = .007).
Conclusions:
More experienced clinicians tend to exhibit greater skepticism toward AI. Larger studies with more detailed data are warranted to clarify these relationships.
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