Acceptability of AI-applications in routine clinical care for children and adolescents: perspectives of parents and healthcare professionals

The study systematically assessed AI acceptability among two stakeholder groups, parents and pediatric healthcare professionals, across different disease, diagnosis, or treatment scenarios, and evaluated the effects of demographic variables and contextual predictors such as data privacy, AI knowledge, and perceived disease severity on willingness to use AI. The text states that these factors had not been systematically assessed across different stakeholder groups, so the study extends acceptability assessment from a single group to parents and clinical staff across multiple clinical scenarios. Based on two parent cohorts (n = 198; n = 79) and pediatric healthcare professionals (n = 33), with reported statistical tests (e.g., p < .001; beta = .10, p = .013).

More liberal data privacy was associated with reduced willingness to use AI. Quantifies the association between data privacy attitudes and acceptability as a contextual predictor rather than only discussing it qualitatively. The text reports a p value of less than .001 for this association.

In the second parent group, higher perceived disease severity was linked to higher willingness to use AI. Suggests that perceived disease severity, as a contextual factor, modulates parents' acceptance of AI, with the result coming from the second parent cohort. Reports a regression coefficient beta = .10, p = .013.

When AI and clinician recommendations conflicted, parents were more likely to choose AI over the judgment of clinicians in treatment compared to diagnosis scenarios; healthcare professionals showed similar patterns but additionally weighted perceived disease severity when resolving disagreements. Reveals how scenario type (treatment vs diagnosis) and role (parent vs healthcare professional) shape trust allocation at the critical decision point of AI-clinician conflict. Based on comparison of choice tendencies in conflict scenarios, distinguishing patterns between parents and healthcare professionals.

The study defines the applicable subjects and settings for acceptability assessment when introducing AI into routine pediatric care: parents (two cohorts) and pediatric healthcare professionals, across different disease, diagnosis, or treatment scenarios. Its findings can guide communication, informed consent, and AI system design for these stakeholders, especially in contexts involving data privacy, disease severity, and AI-clinician recommendation conflicts. Results apply to acceptability discussions in pediatric clinical environments rather than directly generalizing to other specialties or non-clinical AI applications.

Readers may still wonder about: reasons for differences between the first and second parent cohorts; the impact of the small healthcare professional sample on estimate stability; interactions among AI knowledge, demographic variables, and data privacy; and how perceived disease severity was measured across different disease types. The loaded text is a summary-level excerpt and does not provide full scales, model specifications, or all statistical details; these open questions require confirmation in the original methods section.