Legal and Regulatory Considerations for Artificial Intelligence Use in Medical and Radiology Research

Abstract

Background Artificial intelligence (AI) is increasingly integrated into medical imaging and radiology research, improving image acquisition, interpretation, workflow efficiency, and decision support. However, its rapid adoption has introduced complex legal and regulatory challenges involving patient privacy, data governance, algorithm validation, liability, intellectual property, transparency, cybersecurity, and ethical oversight. A comprehensive understanding of the evolving legal landscape is essential for the safe, effective, and legally compliant implementation of AI in radiology. Methods A narrative review of the published literature and current regulatory guidance was conducted. Relevant international legal and regulatory frameworks governing AI in medical imaging were evaluated, including the European Union Artificial Intelligence Act, General Data Protection Regulation, U.S. Food and Drug Administration guidance on Software as a Medical Device (SaMD), Health Insurance Portability and Accountability Act, World Health Organization recommendations, International Medical Device Regulators Forum principles, and selected national regulations. Key topics reviewed included data protection, informed consent, AI validation, regulatory approval pathways, post-market surveillance, cybersecurity, algorithmic bias, explainability, and medico-legal responsibility. Results Current regulatory frameworks increasingly classify AI-enabled radiology software as SaMD, requiring risk-based evaluation, clinical validation, quality management systems, and continuous post-market performance monitoring. Regulatory authorities emphasize transparency, human oversight, data security, and lifecycle governance of AI systems. Nevertheless, important legal challenges remain, including liability for AI-assisted diagnostic errors, ownership of AI-generated outputs, secondary use of imaging data, cross-border data sharing, algorithmic bias, and regulation of adaptive or continuously learning AI models. Conclusions The legal framework governing AI in radiology is evolving in parallel with technological innovation. Responsible integration of AI into clinical practice and research requires harmonized international regulations, robust data governance, standardized validation protocols, transparent algorithm development, and clearly defined legal accountability. Continued collaboration among radiologists, researchers, regulators, policymakers, industry, and legal experts will be essential to ensure that AI technologies remain safe, ethical, equitable, and trustworthy while maximizing their potential to improve patient care.