In the pharmaceutical, biotech, and medical device industries, GxP regulations—including Good Manufacturing Practice (GMP), Good Laboratory Practice (GLP), Good Clinical Practice (GCP), and related standards—ensure product quality, safety, and reliability. Artificial intelligence (AI) and machine learning (ML) applications that affect product development, manufacturing, quality control, clinical outcomes, or regulatory compliance in these industries typically require GxP validation.
Traditional software validation under GxP involves clear, deterministic processes where you test inputs, verify outputs, and document everything for audits. But AI and ML tools introduce a layer of complexity that traditional validation frameworks simply aren’t built to handle. These technologies learn from data, evolve over time, and often operate as black boxes, making compliance a headache for developers and regulators alike.
This article examines the major challenges involved in validating AI/ML tools under GxP and outlines the FDA’s evolving stance on their use. Whether you’re a compliance officer, data scientist, or executive in life sciences, understanding these obstacles is crucial as AI adoption accelerates in drug discovery, manufacturing, and clinical trials.
Key Challenges in Validating AI and ML Tools in GxP Environments
Validating AI/ML in a GxP environment is challenging because of several inherent characteristics of these technologies. Key obstacles include:
- The Black-Box Nature and Lack of Explainability
Regulators such as the FDA and EMA require that AI-driven medical devices or pharmaceutical applications adhere to ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate, plus Complete, Consistent, Enduring, and Available), yet most AI models lack the transparency and traceability to meet these expectations. AI models, especially deep learning ones, often operate as “black boxes,” where inputs lead to outputs without clear insight into the decision-making process. This opacity becomes especially problematic when AI tools are used in clinical decision support (CDS) systems, where erroneous predictions could impact patient safety.In GxP contexts – such as predictive analytics for batch release, defect detection in manufacturing, clinical trial data analysis, or patient stratification – organizations must be able to explain why a model produced a given result to support audit trails and root-cause analysis. Regulators expect decisions to be justifiable based on scientific principles, but without explainability, it becomes difficult to audit or verify why a model made a specific prediction or decision.
Techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) are emerging, but integrating them into validation adds complexity and computational overhead. “Explainability by Design” is recommended, favoring inherently interpretable AI models, such as decision trees or rule-based systems, whose internal logic can be directly understood without requiring additional tools or post-hoc analysis. Without built-in interpretability, meeting GxP requirements for traceability and change control becomes significantly harder. Validation teams must now incorporate explainability as a core qualification criterion, often adopting hybrid approaches that blend AI with rule-based logic.
- Dynamic Learning vs. Static Validation
Traditional validation depends on reproducible outcomes –the same input s should consistently produce the same output every time. AI/ML models, particularly those built on neural networks or deep learning, introduce variability. Stochastic elements such as random initialization during training or probabilistic decision-making can cause outputs to vary slightly across runs, even when the inputs are identical.
Additionally, adaptive AI models add another layer of complexity as they continuously learn from new data and respond to shifting real-world conditions, their performance evolves in a way that is unpredictable. This variability complicates Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ). How do you qualify a system that evolves over time and may produce different outputs?
Regulators expect evidence of consistency, yet AI’s inherent variability demands custom statistical thresholds and extensive simulation testing to prove reliability. In GxP environments, this can lengthen validation cycles and increase costs, as teams must document every training iteration and actively manage model drift. Solutions like data-lineage tracking and automated ML pipelines can help, but they introduce their own validation requirements , creating a recursive compliance challenge.
- Data Quality Issues
AI/ML performance depends heavily on the quality of training data, which must comply to ALCOA+ principles. Sourcing high-quality, representative datasets for training is a massive challenge in life sciences, where data is often siloed, incomplete, or restricted due to patient-privacy requirements such as HIPAA compliance. In GxP, data pipelines must maintain an unbroken “digital thread” to ensure full provenance, and all inputs be controlled, versioned, and qualified.
Handling large volumes of real-world data introduces risks of inconsistencies, labeling errors, and/or incomplete records. Poor data quality leads to models that generalize poorly, violating GxP’s fitness-for-use principle. Biases in data can enter the data set undetected – for example underrepresentation of minority demographics in clinical trial data – resulting in non-compliant outcomes or decisions that jeopardize product quality or patient safety
AI and ML system validation requires rigorous data-governance controls, along with robust methods for detecting and mitigating bias. These tasks go beyond traditional validation practices, which were never designed to handle the scale, variability, and sensitivity of modern AI training data.
- Cybersecurity and Data Integrity Risks
GxP requires stringent controls over data integrity, yet AI/ML systems are vulnerable to:
- Adversarial attacks where manipulated inputs are crafted to mislead or destabilize data models.
- Model inversion attacks which can expose sensitive training data by reconstructing it from AI outputs.
- Supply chain compromises such as tainted training datasets or malicious open-source libraries embedded in the development pipeline.
The FDA’s Perspective on AI/ML Validation in GxP
The FDA has taken a proactive stance on AI/ML in regulated industries, recognizing both its potential and risks. Their approach emphasizes a risk-based, Total Product Lifecycle (TPLC) framework designed to promote innovation while maintaining strong protections for public health.
Recent guidance documents focus on safeguarding public health by ensuring effectiveness, quality, and regulatory compliance through risk-based evaluations, credibility assessments, and lifecycle management. Key guidance documents include:
For Drugs and Biological Products (Pharmaceutical and Biotech Industries)
- Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products (Draft Guidance, January 2025): This document outlines FDA recommendations for sponsors using AI to generate data or analyses that inform regulatory decisions related to drug safety, effectiveness, or quality. It emphasizes a risk-based framework to establish AI model credibility within a defined context of use, including data quality, model development practices, performance evaluation, and ongoing monitoring to detect bias and ensure reliability. It draws from more than 500 AI-related submissions the FDA has reviewed since 2016.
- Guiding Principles of Good AI Practice in Drug Development (January 2026): Developed collaboratively by the FDA and EMA, this document outlines ten principles for responsible AI use in drug development. Several principles directly relate to validation – including requirements for ensuring model transparency, robustness, fairness, rigorous testing, and bias mitigation. It also emphasizes the need for meaningful human oversight and promotes continuous learning and adaptation provided that the AI-generated outputs used in regulatory submissions are appropriately validated.
- Artificial Intelligence in Drug Manufacturing (Discussion Paper, 2023, with ongoing relevance): This paper explores the use of AI in pharmaceutical manufacturing and highlights key validation challenges, such as model development for process control, data-management integrity, and avoidance of bias. It underscores the need for clear standards to validate AI systems used in release testing and quality assurance – standards that may shape future regulatory guidance.
For Medical Devices
- Good Machine Learning Practice for Medical Device Development: Guiding Principles (December 2025): This document outlines ten principles to support the development of safe, effective, and high-quality AI/ML-enabled medical devices. Key validation-related elements include leveraging multi-disciplinary expertise, strong software engineering, security practices, and the use of clinically relevant data for training and testing. The principles ensure model independence from training data; and focuses on human-AI interaction for usability. Post-market monitoring and robust risk-management practices are highlighted as essential for validating performance across diverse users and real-world conditions.
- Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions (Final Guidance, August 2025): The FDA guidance provides recommendations for including a Predetermined Change Control Plan (PCCP) in marketing submissions for AI-enabled device software functions (AI-DSFs). An authorized PCCP allows manufacturers to implement iterative modifications without requiring additional premarket reviews while ensuring safety and effectiveness. Applicable to 510(k), De Novo, and PMA pathways, the guidance builds on the 2019 discussion paper and FDORA. Modifications following an authorized PCCP and 21 CFR Part 820 quality requirements generally avoid new submissions, while deviations or out-of-plan changes may require them.
- Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations (Draft Guidance, January 6, 2025): Building on prior work such as the 2021 AI/ML SaMD Action Plan, the draft guidance proposes lifecycle-oriented expectations for AI-enabled devices. It addresses validation through premarket reviews, structured change-control plans, and transparency requirements. The document also highlights AI-specific validation topics such as bias mitigation, performance monitoring, and managing model adaptability over time.
Cross-Industry Guidance
- Computer Software Assurance for Production and Quality System Software (September 2025): This guidance applies to both pharmaceutical and biotech manufacturing as well as medical devices, recommending a risk-based approach to software validation, including AI/ML tools used in production or quality systems. It supports the use of unscripted testing, continuous monitoring, and leveraging vendor-supplied validations to establish confidence while ensuring compliance with 21 CFR Part 820 and minimizing unnecessary validation burden.
Together, these documents reflect the FDA’s evolving approach to AI and ML, prioritizing risk-based validation to foster innovation while safeguarding public health.
Conclusion
The FDA’s guidance signals a supportive yet cautious stance on AI, prioritizing patient safety amid innovation. The core challenges of GxP validation for AI/ML – reproducibility, opacity, data integrity, and lifecycle flux – demand a shift from rigid testing to agile, risk-based strategies. By leveraging AI maturity model frameworks and AI-enabled validation tools such as automated prompt testing, organizations can scale compliance activities while building trust in their systems.
As regulatory expectations continue to evolve, staying informed and engaged through ISPE resources, FDA workshops, and industry forums will be crucial. For life-science leaders, the key is to integrate AI thoughtfully and deliberately: start with pilots, assess risks early, and maintain meaningful human-in-the-loop oversight. As AI reshapes life sciences, mastering these validation challenges will not only ensure compliance – it will redefine GxP operations and create new competitive advantages in efficiency, quality, and safety.
