Data Privacy, Compliance, and Security Including AI ML
Sangeeta Singhal · Advances in systems analysis, software engineering, and high performance computing book series · 2024
Ensuring data privacy, compliance, and security in healthcare settings, particularly with the integration of artificial intelligence (AI) and machine learning (ML), is paramount for safeguarding sensitive patient information and maintaining trust. The intersection of these technologies with healthcare data introduces unique challenges, including patient confidentiality, regulatory compliance (e.g., HIPAA, GDPR), and the risk of data breaches. While AI and ML hold tremendous potential for improving patient care and treatment outcomes, they also raise concerns regarding algorithmic bias, fairness, and interpretability. To address these challenges, healthcare organizations must implement robust data security measures such as encryption, anonymization, and access controls, while also prioritizing transparency and accountability in AI-driven decision-making processes. Emerging trends in privacy-preserving techniques, such as federated learning and differential privacy, offer promising solutions for balancing innovation with patient rights and regulatory requirements.