Operationalizing NIST AI RMF in Pediatric Behavioral Analytics

Abstract

The fast adoption of Artificial Intelligence (AI) in pediatric behavioral analytics is associated with clinical opportunities and governance challenges. This paper constructs a lean, operationalized framework that relies on the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF) in order to establish safe, transparent, and auditable AI use in the analysis of autism behavior. The model proposes a small control catalog, audit checklist, and incident drill protocol which have been tested in three small healthcare institutions. Findings show that compliance efficiency has increased by 35 per cent and that the audit preparation time has decreased by 28 per cent. The results show that risk-conscious AI governance may be viable to be introduced into the pediatric behavioral ecosystems without sacrificing innovation or trust.