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Tomašev, N., Glorot, X., Rae, J.W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., Connell, A., Hughes, C.O., Karthikesalingam, A., Cornebise, J., Montgomery, H., Rees, G., Laing, C., Baker, C.R., Peterson, K. and Reeves, R. (2019) A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury. Nature, 572, 116-119.
https://doi.org/10.1038/s41586-019-1390-1

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• TITLE: Cross-Regional Analysis of Global AI Healthcare Regulation

  AUTHORS: Pravin Ullagaddi

  KEYWORDS: Artificial Intelligence, Machine Learning, Healthcare Regulation, FDA, EMA, NMPA, Healthcare Policy, Global Governance

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.5, May 26, 2025

  ABSTRACT: The rapid integration of artificial intelligence (AI) into healthcare has the potential to revolutionize pharmaceutical and medical device industries, offering unprecedented opportunities for innovation and improved patient care. However, these advancements necessitate robust regulatory frameworks to ensure safety, efficacy, and ethical implementation. This paper critically examines key regulatory guidelines issued by leading agencies worldwide, including the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), China’s National Medical Products Administration (NMPA), and Singapore’s Health Sciences Authority (HSA). Through comprehensive analysis of regulatory approaches across diverse healthcare systems and governance models, we identify five critical convergent themes: risk-based frameworks, transparency requirements, lifecycle management strategies, ethical considerations, and global harmonization efforts. The research methodology combines systematic review of regulatory documents with analysis of implementation cases, revealing significant variation in how jurisdictions balance innovation facilitation with patient protection. Our findings highlight both the challenges and opportunities in developing internationally coherent AI governance, with particular focus on emerging models from East Asia and other regions that offer alternative approaches to Western frameworks. By examining cross-regional regulatory strategies, this work provides practical insights for policymakers, industry leaders, and healthcare providers navigating the complex landscape of AI regulation. We conclude that successful AI integration in healthcare depends on adaptive regulatory models that address algorithmic bias, data privacy, and equitable access while fostering innovation through international collaboration, regulatory sandboxes, and public-private partnerships.