Federated AI for Trustworthy Clinical Decision Support: Privacy-Preserving Integration of Workforce, Autism Care, and Predictive Health Monitoring

Abstract

Artificial Intelligence (AI) has revolutionised the healthcare space with predictive modelling, clinical decision support system (CDSS) and personalised intervention. Yet, hurdles exist with regards to data privacy, trust from workforce and integration of care for people with autism. This research presents a federal AI framework that is augmented with differential privacy guarantees that brings together clinical workforce planning, autism monitoring, and models for fraud detection-based anomaly detection. Using a hybrid Bayesian-reinforcement learning architecture on nodes of distributed health care and workforce data, the system has better predictive accuracy and protects sensitive data. Results show the 12% increase in accuracy with the 8% decrease of false positive compared to baseline centralized models. Federated privacy-preserving design to ensure scalability and also to comply with ethical AI principles. This study offers one of the first integrated strategies to balance clinical trust with caregiver usability with technical rigor, and paves the way for future large-scale validation in autism care and beyond, for precision medicine.

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