Generative AI in Healthcare Services: Opportunities, Risks, and Implementation Frameworks

Abstract

Generative artificial intelligence (GenAI) has emerged as a transformative technology in healthcare, enabling new forms of automation, synthesis, and clinical decision support. By leveraging advanced models such as large language models (LLMs) and multimodal AI systems, healthcare organizations can streamline documentation, improve patient engagement, assist in diagnostics, and support drug discovery. However, these benefits come with risks including hallucinated outputs, bias, privacy threats, and regulatory uncertainty. This review outlines the major opportunities for GenAI in healthcare, examines potential hazards, and discusses global implementation frameworks such as the World Health Organization’s guidance for large multimodal models, the European Union’s AI Act, the U.S. Food and Drug Administration’s policy updates, and the NIST AI Risk Management Framework. A structured “SAFE-DEPLOY” approach is proposed to guide responsible integration, covering scope definition, risk assessment, human oversight, regulatory compliance, and continuous monitoring.

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