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<p>Artificial Intelligence-Generated Content (AIGC) represents a paradigm shift in biomedical research and healthcare delivery, offering unprecedented capabilities for content creation, medical data analysis, and patient care optimization. This review examines the evolution of AIGC technologies from rule-based systems to advanced multimodal large models, with specific focus on their applications in healthcare settings. We analyze the three core capabilities of AIGC: intelligent digital content twinning, editing, and creation, and their transformative potential in medical imaging, clinical documentation, drug discovery, and personalized medicine. This paper discusses key challenges including algorithmic transparency, data privacy, and regulatory compliance, particularly in light of World Health Organization (WHO) guidelines for AI in health. Our findings indicate that while AIGC technologies show remarkable promise in enhancing diagnostic accuracy, streamlining clinical workflows, and democratizing healthcare access, careful consideration of ethical implications and regulatory frameworks is essential for safe and effective implementation.</p>

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Artificial intelligence-generated content (AIGC) in biomedical research, healthcare delivery, and clinical practices: technologies, applications, and regulatory considerations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月19日

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  • Jiancheng Ye

摘要

Artificial Intelligence-Generated Content (AIGC) represents a paradigm shift in biomedical research and healthcare delivery, offering unprecedented capabilities for content creation, medical data analysis, and patient care optimization. This review examines the evolution of AIGC technologies from rule-based systems to advanced multimodal large models, with specific focus on their applications in healthcare settings. We analyze the three core capabilities of AIGC: intelligent digital content twinning, editing, and creation, and their transformative potential in medical imaging, clinical documentation, drug discovery, and personalized medicine. This paper discusses key challenges including algorithmic transparency, data privacy, and regulatory compliance, particularly in light of World Health Organization (WHO) guidelines for AI in health. Our findings indicate that while AIGC technologies show remarkable promise in enhancing diagnostic accuracy, streamlining clinical workflows, and democratizing healthcare access, careful consideration of ethical implications and regulatory frameworks is essential for safe and effective implementation.