Regulatory and Social Acceptance Challenges in Using Artificial Intelligence in Genomic Diagnostics in Saudi Arabia: Applying the Responsive Regulation and Innovation Diffusion Model.

Abstract

This study explores the regulatory and social acceptance challenges of integrating Artificial Intelligence (AI) into genomic diagnostics in Saudi Arabia, using the Responsive Regulation and Innovation Diffusion model as theoretical frameworks. Methodology: The research employs a narrative review methodology, emphasizing regulatory frameworks, public trust, and the cultural perceptions that influence the adoption of AI technologies. Findings: The study identifies that, although AI holds significant promise for advancing genomic diagnostics, its full integration is hindered by regulatory gaps and a low level of social acceptance. The research emphasises the importance of creating a flexible and dynamic regulatory framework that can evolve with AI advancements. It also highlights the crucial role of stakeholder engagement and public education in building trust and ensuring that innovation progresses without compromising public safety. Limitations: Key limitations of the study include the restricted scope of the literature review, which primarily focuses on the European Union and Saudi Arabia, and the fast-paced development of AI technology, which may limit the long-term applicability of the proposed models. Practical Implications: To improve the adoption of AI in healthcare, this study recommends the implementation of regulatory sandboxes, which would allow AI innovations to be tested in controlled environments. Additionally, fostering public trust through transparency and education is critical to ensuring the successful integration of AI technologies in genomic diagnostics.