Challenges and Strategies in Data Management and Governance for AI-Based Healthcare Models: Balancing Innovation and Ethical Responsibilities

Abstract

The advent of artificial intelligence (AI) in healthcare presents unprecedented opportunities for improving patient outcomes and healthcare efficiency. However, it also introduces significant challenges in data management and governance, particularly in balancing the drive for innovation with ethical responsibilities. This paper explores the multifaceted challenges of ensuring data privacy and security, maintaining data quality and integrity, addressing ethical concerns, achieving regulatory compliance, enhancing interoperability, and fostering sustainable innovation in the context of AI in healthcare. We propose a comprehensive set of strategies, including robust encryption and access controls, data standardization protocols, ethical guidelines for AI use, and adherence to evolving regulatory frameworks. The paper emphasizes the importance of informed consent and transparency in patient data use, and advocates for a collaborative approach involving stakeholders across the healthcare spectrum. Our analysis underscores the necessity of a multidisciplinary approach in managing and governing healthcare data in the AI era, focusing on patient-centric solutions while navigating the ethical and regulatory landscapes. This study contributes to the ongoing discourse on AI in healthcare, offering insights and frameworks for practitioners, policymakers, and researchers engaged in this transformative field.