Background: In clinical settings, clinicians’ context-specific trust in AI is associated with individual factors. This 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. Therefore, examining potential biases in context-specific trust and achieving well-calibrated trust are critical. Objective: This study aimed to examine the individual factors associated with professionals’ context-specific trust in AI-assisted ASD screening systems. Methods: Fifty-five professionals involved in ASD assessment participated in the study. We used an AI-based ASD screening method based on a geometric shape-drawing task that assesses children’s motor function and has demonstrated high sensitivity for identifying children with ASD. Expert clinicians selected 2 ambiguous screening scenarios for evaluation. Participants made independent judgments while considering the AI output and rated their context-specific trust in each decision on a 7-point Likert scale. A linear mixed model (LMM) examined the associations between context-specific trust in AI decisions and participant characteristics while accounting for the nested data structure. Results: LMM analysis revealed significant between-participant variability in context-specific trust. Years of professional experience (=−2.20; =.03) and the length of education required to obtain professional credentials (=−2.67; =.01) were significantly and negatively associated with context-specific trust in AI-based screening. Dispositional trust in AI was significantly and positively associated with context-specific trust (=2.78; =.008). Confidence in participants’ own assessments was significantly and positively associated with context-specific trust (=5.67;

The House Judiciary Juvenile Committee unanimously approved HB 943, creating a multi-region pilot to screen, evaluate and treat foster children for autism spectrum disorder and requiring periodic reporting on implementation and effectiveness.