Artificial intelligence in the non-clinical laboratory: enhancing good laboratory and documentation practices.

Abstract

Non-clinical laboratories are under increasing regulatory pressure from agencies such as the FDA, EMA, and MHRA to ensure data integrity, traceability, and compliance with Good Laboratory Practice (GLP) and Good Documentation Practice (GDP). While Artificial Intelligence (AI) has been widely explored in clinical diagnostics and drug discovery, its application to non-clinical laboratories, particularly in relation to validation, regulatory alignment, and Technology Readiness Level (TRL), remains limited. This review critically examines how AI can strengthen GLP/GDP compliance through applications in anomaly detection, predictive modeling, computer vision, and natural language processing. Unlike existing reviews that emphasize technical algorithms, this work highlights regulatory dimensions, including risk-based validation protocols, integration with ICH guidelines (Q8-Q14), and compliance with frameworks such as FDA, EMA, and ALCOA + principles. The maturity of AI tools is assessed using TRL mapping, which differentiates between deployable applications, such as Random Forest models for predictive quality control and AI-enhanced Laboratory Information Management Systems (LIMS) for audit trail automation, and speculative, early-stage concepts, including NLP-driven audit documentation. Current barriers include limited interoperability with legacy systems, insufficient workforce training, and high infrastructure costs, particularly in low- and middle-income countries. To address these challenges, phased adoption strategies using open-source tools, cloud-based platforms, and human-in-the-loop oversight are proposed to ensure transparency and regulatory acceptability. By linking AI adoption with Pharma 4.0, Process Analytical Technology (PAT), and Quality by Design (QbD), this review provides a structured roadmap for regulators, practitioners, and technology developers. In doing so, it advances the discussion beyond technical feasibility to focus on compliance, scalability, and equitable access, ensuring that AI enhances rather than disrupts laboratory quality practices.