Regulatory and legal challenges of Artificial Intelligence in the U.S. Healthcare System: Liability, Compliance, and Patient Safety

Osifowokan, Adewale Samuel and Agbadamasi, Tessy Oghenerobovwe and Adukpo, Tobias Kwame and Mensah, Nicholas (2025) Regulatory and legal challenges of Artificial Intelligence in the U.S. Healthcare System: Liability, Compliance, and Patient Safety. World Journal of Advanced Research and Reviews, 25 (3). pp. 949-955. ISSN 2581-9615

This study explores Regulatory and Legal Challenges of Artificial Intelligence in the U.S. Healthcare System: Liability, Compliance, and Patient Safety. It also examines the challenges associated with AI integration, including ethical concerns, data privacy risks, and regulatory compliance, as providing insights into legal frameworks governing AI in healthcare. A qualitative research approach was employed, involving a comprehensive review of existing literature, and regulatory policies. Peer-reviewed journals, government publications, and industry reports were analyzed to assess the effectiveness, challenges, and future implications of AI-driven healthcare solutions. The study reveals that AI significantly enhances healthcare delivery by improving diagnostic accuracy, enabling personalized treatments, and optimizing hospital workflows. Machine learning models and natural language processing facilitate early disease detection, while robotic process automation streamlines administrative processes. However, challenges such as algorithmic bias, data security concerns, and the need for stringent regulatory oversight persist. Regulatory frameworks such as HIPAA, GDPR, and FDA guidelines provide necessary compliance structures but require continuous updates to keep pace with AI advancements. The paper therefore concludes that AI is revolutionizing healthcare, offering significant benefits in efficiency and patient outcomes. However, successful implementation necessitates a balanced approach that integrates ethical considerations, data protection measures, and regulatory frameworks. Future research should focus on enhancing AI transparency, addressing biases, and ensuring that AI-driven healthcare solutions remain patient-centered and legally compliant.