This article explores the integration of adaptive AI and quantum computing to combat financial fraud and cyber-attacks in the U.S. healthcare sector. By leveraging deep neural networks, reinforcement learning, and quantum-enhanced models, we propose a hybrid framework capable of achieving high fraud detection accuracy and anomaly detection in real-time. Case studies and empirical evaluations demonstrate the superiority of the framework over traditional methods, while ethical and regulatory implications are addressed to ensure responsible deployment.