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The integration of artificial intelligence (AI) into healthcare systems has revolutionized various facets of modern medicine, fundamentally transforming clinical diagnostics, enabling more precise treatment personalization, optimizing administrative processes, and enhancing patient engagement and monitoring. AI-powered technologies such as predictive analytics, medical imaging interpretation, virtual health assistants, and genomic data analysis have accelerated the delivery of personalized and efficient healthcare services. Despite these transformative benefits, the rapid proliferation of AI in healthcare introduces a complex spectrum of ethical, legal, and privacy-related challenges that demand urgent attention. Key concerns include data privacy violations stemming from the extensive use of sensitive patient information, algorithmic bias that may exacerbate existing healthcare disparities, lack of transparency in AI decision-making processes, and regulatory fragmentation that hinders the establishment of universally accepted ethical standards. This review critically examines these emerging concerns by synthesizing current empirical studies, regulatory frameworks, practical case examples, and international guidelines on AI governance. In response to these challenges, we introduce the CARE framework—encompassing Consent, Auditability, Responsibility, and Equity—as a comprehensive ethical model designed to guide the responsible development, deployment, and oversight of AI technologies within healthcare. The framework emphasizes continuous patient consent, transparent and explainable algorithms, distributed accountability among stakeholders, and equity-driven system design to mitigate bias and promote fairness. Positioned within the broader global policy discourse, this framework offers actionable pathways for policymakers, healthcare providers, and AI developers to align technological innovation with foundational ethical principles. The paper concludes by underscoring the necessity of institutional accountability, fostering ethical literacy across disciplines, and advocating for harmonized international policies to ensure trust, fairness, and sustainability in the future of healthcare AI.

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Privacy and Ethics in Healthcare AI: A Review of Current Challenges and a Policy-Driven Framework for Responsible Innovation

  • 会议论文

  • 发表日期：

    2026年

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  • Shifan Khanday

摘要

The integration of artificial intelligence (AI) into healthcare systems has revolutionized various facets of modern medicine, fundamentally transforming clinical diagnostics, enabling more precise treatment personalization, optimizing administrative processes, and enhancing patient engagement and monitoring. AI-powered technologies such as predictive analytics, medical imaging interpretation, virtual health assistants, and genomic data analysis have accelerated the delivery of personalized and efficient healthcare services. Despite these transformative benefits, the rapid proliferation of AI in healthcare introduces a complex spectrum of ethical, legal, and privacy-related challenges that demand urgent attention. Key concerns include data privacy violations stemming from the extensive use of sensitive patient information, algorithmic bias that may exacerbate existing healthcare disparities, lack of transparency in AI decision-making processes, and regulatory fragmentation that hinders the establishment of universally accepted ethical standards. This review critically examines these emerging concerns by synthesizing current empirical studies, regulatory frameworks, practical case examples, and international guidelines on AI governance. In response to these challenges, we introduce the CARE framework—encompassing Consent, Auditability, Responsibility, and Equity—as a comprehensive ethical model designed to guide the responsible development, deployment, and oversight of AI technologies within healthcare. The framework emphasizes continuous patient consent, transparent and explainable algorithms, distributed accountability among stakeholders, and equity-driven system design to mitigate bias and promote fairness. Positioned within the broader global policy discourse, this framework offers actionable pathways for policymakers, healthcare providers, and AI developers to align technological innovation with foundational ethical principles. The paper concludes by underscoring the necessity of institutional accountability, fostering ethical literacy across disciplines, and advocating for harmonized international policies to ensure trust, fairness, and sustainability in the future of healthcare AI.