Recent advances in artificial intelligence (AI) and machine learning have substantially expanded their application in molecular diagnostic technologies. AI-based tools are increasingly integrated into various stages of molecular testing workflows, including primer and probe design, interpretation of real-time and digital PCR data, and high-throughput analysis of next-generation sequencing (NGS) datasets. While these technologies offer significant advantages in terms of analytical speed, accuracy, and reduction of human error, their rapid implementation has outpaced the development of harmonized standards and regulatory frameworks.
This article provides a critical analysis of the emerging challenges associated with the standardization of AI-assisted molecular diagnostics. Current international standards for molecular testing have been primarily designed for conventional analytical methodologies and do not adequately address algorithm-driven decision-making processes. Key challenges discussed include data quality and governance, algorithm training and validation strategies, software version control, model adaptability, and the lack of standardized performance evaluation metrics tailored to AI-based systems. These limitations contribute to inter-laboratory variability, reduced reproducibility, and uncertainty in regulatory acceptance.
The regulatory and societal implications of deploying AI-based molecular diagnostics, particularly in food safety, agriculture, and public health, are also examined. Finally, the article emphasizes the urgent need for dedicated and harmonized standards that encompass analytical performance, algorithm lifecycle management, transparency, and quality assurance. Addressing these gaps is essential for ensuring result reliability, regulatory confidence, and the sustainable integration of AI-driven molecular diagnostic technologies