Equitable and Explainable Federated-Edge AI for Autism Care: Bridging Clinical Innovation and Global Ethical Standards

The changes that artificial intelligence (AI) is making to healthcare involve the field of precision medicine, detecting anomalies, and workforce planning [1-3]. However, in the process of autism care, adoption is not very high because of the behavioral escalations, privacy, and the requirement of trust between the clinician. In this paper, the author suggests a fair federated-edge artificial intelligence framework, which incorporates speech, movement, and physiological tracking into a safe and transparent clinical decision-support system (CDSS). As opposed to centralized systems, the system uses federated privacy-preserving learning, edge intelligence to do low-latency inference, and human-centric dashboards to offer transparency and actionable workforce advice. Synthetic dataset results show an increase in predictive accuracy, reduction of latency, and usability by clinicians. This model is relevant to autism care; however, it can be used as a transferable basis to support behavioral health and precision medicine in compliance with international AI ethics standards to enhance fairness, accountability, and sustainability.