Securing the Future of Digital Health: Building Trustworthy and Resilient AI Systems
Digital health systems increasingly rely on artificial intelligence and machine learning to enhance clinical decision-making, diagnostic capabilities, and patient outcomes. However, the growing adoption of AI also introduces significant cybersecurity, privacy, and model-integrity challenges. This keynote will explore strategies for building secure, trustworthy, and resilient AI systems for digital health. It will discuss emerging threats such as adversarial machine learning, along with practical approaches including adversarial training, federated learning, privacy-preserving techniques, real-time anomaly detection, and robust model validation. The session will also address regulatory considerations, data heterogeneity, explainability, and the importance of human oversight. Drawing on research and practical perspectives, the keynote will highlight how cybersecurity and clinical effectiveness can be addressed together to support the responsible deployment of intelligent healthcare technologies.
