Artificial intelligence (AI) is transforming healthcare across diagnostics, decision-making, and clinical workflows, yet its integration raises complex ethical, legal, and operational challenges. This narrative review synthesizes three traditionally fragmented domains: Ethical principles, legal accountability, and implementation realities. We draw on literature from major databases alongside regulatory frameworks, including the World Health Organization, the Organisation for Economic Co-operation and Development, the National Institute of Standards and Technology, the European Union (EU), the Food and Drug Administration (FDA), and the International Medical Device Regulators Forum, and examine Türkiye’s policies (e.g., Personal Data Protection Law No. 6698) to provide a middle-income country perspective. This review makes three contributions. First, we reconceptualize core bioethical principles – autonomy, beneficence, non-maleficence, and justice – in AI-mediated settings, emphasizing transparency, human oversight, and equity-sensitive design. Second, we frame legal accountability as a distributed system involving developers, institutions, and clinicians. Third, we bridge theory and practice through real-world cases (sepsis prediction vs. proprietary algorithms) and propose an integrated lifecycle governance model. Comparative analysis of the EU AI Act, FDA’s 2026 guidance, and Türkiye’s regulatory landscape shows convergence toward risk-based governance, alongside persistent gaps, particularly in middle-income settings. Responsible AI governance requires not only regulatory compliance but also continuous evaluation, transparency, and human-centered oversight. Despite global convergence on high-level principles, significant gaps remain in translating these into enforceable mechanisms and clinical practice. Future research should prioritize empirically validated governance models that ensure AI augments – rather than undermines – clinical judgment and patient trust.