Executive Summary

The adoption of AI and ML in regulated life-sciences (GxP) environments offers vast potential for efficiency, quality, and innovation. However, these systems introduce new challenges for validation and compliance. Unlike traditional rule-based software, AI/ML systems (especially dynamic models that learn post-deployment) require novel approaches to ensure they remain reliable, accurate, and auditable. This report examines how the GAMP® 5 (Good Automated Manufacturing Practice) risk-based approach can be extended to AI/ML in GxP, summarizing current thinking, guidelines, and practices. We review industry perspectives and regulatory expectations—ranging from FDA/EMA frameworks to ISPE guidance—highlighting key principles such as risk-based validation, data integrity (ALCOA+), and lifecycle management. We present classifications of AI systems (e.g., locked/static vs. continually adaptive) and describe how each fits into GAMP’s lifecycle phases. Case examples (e.g. AI in sterile manufacturing, pharmacovigilance automation) illustrate practical solutions. Throughout, we emphasize evidence-based strategies: defining clear intended use, rigorous data governance, robust testing with checkpoints, and human oversight embedded in standard operating procedures. In conclusion, existing GAMP principles remain fundamentally valid, but must be augmented with additional controls (e.g. monitoring for model drift, treating AI inputs/outputs as electronic records (^[1]) (^[2])). The future of AI in GxP will be shaped by ongoing regulatory evolution (e.g. new EU Annex 22 for AI (^[3]), FDA/IMDRF machine-learning guidelines (^[4])) and by deeper integration of data science with quality systems. By adopting a rigorous, risk-driven approach aligned with GAMP 5, organizations can leverage AI’s benefits while ensuring patient safety, product quality, and data integrity.

Introduction and Background

Good practice regulations (GxP) – including Good Manufacturing Practices (GMP), Good Clinical Practices (GCP), and Good Laboratory Practices (GLP) – require that computerized systems impacting patient safety and product quality be validated through a risk-based lifecycle (^[5]) (^[6]). Traditionally, this Computerized System Validation (CSV) involved static, rule-based software examined via specifications, testing, and documentation (e.g. 21 CFR Part 11 (electronic records) and EU Annex 11 for computerized systems). GAMP® 5 provides a seminal risk-based framework for compliant GxP computerized systems, assigning complexity categories and emphasizing quality management at every stage (^[7]) (^[8]).

Recently, advanced AI/ML technologies – from image-recognition models to generative language models – are entering regulated domains (Figure 1). These systems can learn from data, optimize processes, and assist decision-making in R&D, manufacturing, and quality operations. For example, AI can analyze bioreactor telemetry for early deviation signals, automate optics inspection on sterile lines, or help in pharmacovigilance case triage. Such AI-driven tools promise productivity gains (e.g. McKinsey projects $60–110 billion/year in pharma/medical-device productivity (^[9])) and reduced workload for routine tasks (^[9]). At the same time, regulators (FDA, EMA, etc.) have signaled that all AI outputs and processes must still meet GxP criteria: electronic records must be trustworthy, attributable, and auditable (^[10]), and data integrity (ALCOA+) principles apply to AI inputs, models, and outputs (^[2]) (^[11]).

The central question is how to adapt GAMP 5 to AI/ML systems. Do we need entirely new frameworks, or can traditional CSV be extended? Industry experts suggest a hybrid approach: retain GAMP’s risk-based lifecycle, documentation, and QMS controls, but augment them with AI-specific activities (e.g. careful training data management, model performance testing, drift monitoring) (^[12]) (^[13]). This report delves deeply into these issues: we will outline regulatory context, classify AI/ML system types in GxP, analyze validation methods (both traditional and AI-tailored), and present evidence (studies, case examples, expert guidance) to show how validation can ensure safety, quality, and compliance.

Regulatory and Standards Context

Core GxP Requirements

In GxP-regulated industries, electronic records and signatures are governed by specific regulations. For example, FDA’s 21 CFR Part 11 (and similar EU Annex 11/Annex 11 guidance) requires that computerized systems maintain records that are trustworthy, reproducible, and auditable (^[10]). ISPE’s GAMP® 5 explains that CSV must be risk-based, focusing on critical process impact (^[7]) (^[10]). Key principles include comprehensive documentation, quality risk management (QRM), traceability, access controls, audit trails, and change control. Good documentation practices demand that each record be ALCOA+ (Attributable, Legible, Contemporaneous, Original, Accurate, plus Complete, Consistent, Enduring, and Available) (^[2]). GAMP 5’s cornerstone is to allocate effort based on risk – simpler systems get lighter validation, complex ones more scrutiny.

These foundations do not change for AI. As one industry review observes, “the shift with AI is not in principle but in practice” – regulators expect extension of standard controls into AI workflows (^[14]). For instance, outputs of an AI model used in production must be treated as electronic records with full traceability (^[11]). Recent regulatory documents underscore this: EMA’s AI Lifecycle Reflection Paper stresses transparency and human oversight throughout the AI lifecycle, FDA guidance calls for data lineage and model validity (^[6]), and ISPE’s GAMP 5 (Second Edition) now explicitly includes appendices on AI/ML(subsystems) (^[15]). Furthermore, the new EU GMP Annex 22 (released 2023) explicitly addresses AI/ML in manufacturing (^[3]) </current_article_content>(^[16]), while health agencies are aligning around global Good Machine Learning Practice principles (^[4]).

In sum, AI/ML in GxP must comply with the same regulatory expectations as other computerized systems: validated, documented, risk-managed, and audit-ready. The difference is that AI’s data-driven, adaptive nature introduces new sources of risk (see next section). Below we explore those unique aspects and how GAMP’s lifecycle approach can accommodate them.

Unique Characteristics of AI/ML Systems

AI/ML systems differ from rule-based software in several ways:

• Data-Driven Learning: Unlike fixed code, AI models are generated from training data. Their behavior is inherently tied to the datasets used for learning (^[17]) (^[2]). Thus data quality is paramount: biased or flawed data yields unreliable models. The FDA/IMDRF explicitly note that “data quality directly determines model quality” (^[2]), and regulators demand ALCOA+ controls on all data used in AI (including training sets, annotations, inputs, and outputs) (^[2]) (^[11]). In practice, this means every AI training dataset, prompt, and result must be documented and traceable as an electronic record (^[11]).

• Opacity and Adaptivity: Many AI models (e.g. deep learning) act as “black boxes” – their internal logic is not human-interpretable. This opacity challenges validation and introspection. Furthermore, models may be updated or “retrained” over time, especially online learning systems. If an AI adapts post-deployment, it can “drift” or change performance. Traditional CSV assumes a static system; dynamic AI requires continuous monitoring and re-validation triggers (^[18]) (^[19]). For example, Huysentruyt et al. note that “operational ML subsystems provide different outputs as they evolve”, so verification must be continuously updated with change control and monitoring, using large validation datasets and robust metrics (^[19]).

• Stochastic Outputs: Some AI (e.g. neural networks) have inherent randomness (e.g. dropout, random weights). Identical inputs can yield slightly different outputs in successive runs. This requires statistical approaches to validation: rather than a single pass/fail test, validation uses sufficiently large test sets and performance summaries (accuracy, precision, recall, etc.) that are meaningful and robust to such variation (^[19]) (^[20]).

• Integration of ML Subsystems: An AI/ML system is often only a component of a larger GxP system. As Staib et al. explain, an “ML component” usually consists of multiple stages (data prep, model, output filtering) and must be managed as an ML subsystem within the wider application (^[20]) (^[21]). The entire pipeline – from data import through model prediction to output – must be controlled. Many aspects of traditional CSV still apply (user interface, access control, reporting, etc.) (^[20]), but new deliverables may be needed (e.g. documented training records, model explainability assessments) to ensure overall compliance.

In summary, AI/ML systems introduce new potential risks (data bias, model drift, opaque decision logic) on top of the usual GxP concerns. Validating them requires not only the standard specification-verification tests, but also rigorous data governance and lifecycle management of the model itself (^[2]) (^[22]).

A GAMP 5 Risk-Based Lifecycle for AI/ML

Despite these new challenges, the GAMP 5 framework remains the foundation for validation. GAMP 5’s risk-based lifecycle can be applied to AI/ML by mapping AI-specific activities into the familiar phases: Concept, Specification, Design/Development, Testing/Validation, and Operation/Maintenance. Below we outline how a GAMP-style approach can cover AI/ML systems, drawing on published guidance and industry practice.

Lifecycle Phases and Deliverables

An ML subsystem’s lifecycle generally parallels the system-level lifecycle (^[8]) (^[13]). For example, ISPE’s Data Integrity Guide (Appendix S1 on AI/ML (^[8])) and GAMP 5 Appendix D11 (^[15]) both describe an ML lifecycle with phases concept, project/production, and operation consistent with GAMP. Table 1 summarizes key deliverables and controls in each phase for AI/ML:

Table 1. Phases of a risk-based lifecycle for AI/ML systems in GxP (adapted from GAMP 5 and industry sources (^[8]) (^[13])).

This table illustrates that all GAMP phases still apply, but with AI-specific tasks woven in. For instance, under data integrity, the user requirements (Phase 1) and data specifications (Phase 2) must fully account for ALCOA+ compliance (^[2]). Under design, model development is integrated with strong versioning and documentation (^[13]) (^[27]). Under validation and testing, statistical performance testing and explainability checks supplement functional tests (^[19]) (^[30]). Finally, operation requires ongoing monitoring dashboards and linking alerts to CAPA processes (^[29]).

Classification of AI/ML Systems in GxP

A helpful starting point (from pharmacovigilance literature) is to classify AI systems by how they behave in production (^[31]). Table 2 (based on Huysentruyt et al. (^[31])) illustrates this:

Table 2. Classification of AI-driven GxP systems and validation framework status (from pharmacovigilance industry guidance (^[31])).

This classification highlights that rule-based static systems – already common in GxP (e.g. Automated Labelling, RPA) – are covered by existing GxP CSV guidance. In contrast, AI-based static systems (Table cell 2) have emerged recently; industry suggests we can “extend” current frameworks (e.g. supplement standard CSV tests with AI-specific evidence) (^[33]). Fully dynamic learning systems (Table cell 3) are the future frontier, currently lacking best-practice frameworks (^[34]). Most current use-cases in pharma/biotech fall into the first two categories; thus much of the focus in this report is on how to validate AI “locked” models under GAMP.

Risk-Based Approach

GAMP 5 is fundamentally risk-based: it teaches that the rigor of validation should match the risk posed by the system to patient/product. This risk-based mind-set applies directly to AI: we must assess the context of use (COU) and potential impact of each AI function (^[23]) (^[35]). For example, Korrapati et al. recommend explicitly linking an AI feature to patient/quality risk: AI used for direct clinical decisions = high risk, requiring device-level validation rigor; AI used for routine documentation (e.g. auto-filing reports) = lower risk, needing proportionate checks (^[23]). This tiered approach (Table 3) is consistent with GAMP’s core theme of scaled validation:

Table 3. Risk-based validation intensity for AI functions (proposed by industry experts (^[23]) (^[36])).

In practice, this means the validation plan must classify each AI function and tailor the approach accordingly (^[23]). Low-risk AI features (such as predicting inventory levels) still require validation and documentation, but the testing can be less onerous. Critical AI (e.g. patient safety algorithms) must be documented in detail: for instance “diagnosis” models may demand multiple hold-out clinical test sets, performance bounding, and post-launch monitoring comparable to software in a medical device (^[24]).

Aligning with GAMP Controls

All standard GAMP controls remain “non-negotiable” even for AI systems (^[22]). This includes:

• Access and Security: Role-based access for all development/production environments, with strict user authentication for data/model handling (^[22]).
• Audit Trails: Immutable logging of all interactions – not just system parameters, but every training run, data update, and AI-generated action must be traceable (^[22]) (^[11]).
• Change Control: Any modification to data pipelines, algorithms, or model parameters must go through QMS change procedures. For example, model retraining is treated like a release: it must have change control records and re-qualification steps (^[22]) (^[19]).
• Backup/Restore: Data and model artifacts must be backed up (including original training data and any intermediate files) to guard against loss (^[22]).
• Quality Risk Management (QRM): GAMP calls for risk analysis at each phase. For AI, risk analysis should emphasize data risks (e.g. bias risk in training set) and model risks (e.g. drift risk in operation) (^[26]) (^[29]).
• Supplier and Software Support: If using third-party AI/ML tools or cloud services, their qualification and maintenance also falls under CSV scope. One must verify vendors’ processes (system support, updates) to ensure continuous compliance (^[3]) (^[22]).

In short, organizations should integrate AI validations into their existing GxP quality management systems, not treat AI in isolation. Many sources advocate a “data centric” extension of GAMP: e.g. identifying data and metadata as deliverables, and tracking the full data life cycle as part of validation documentation (^[37]) (^[2]).

Data Integrity and Governance

A recurring theme is that Data = Model in AI. Thus, data integrity takes on heightened importance (^[2]). The US Data Integrity guidance and 21 CFR Part 11 principles extend to machine learning data. The GAMP “Records and Data Integrity” guide’s Appendix on AI/ML specifically links the ML data lifecycle to the GAMP system lifecycle (^[8]). In practice:

• ALCOA+: Apply ALCOA+ to all AI data (^[2]). Training datasets, annotations, and preprocessing steps must be attributable (record source of data), original (retain raw data), accurate/complete (clean and label data properly), enduring/available (keep copies of data indefinitely and usable). For example, Korrapati et al. state that onboarding data “as rigorously as any regulated component” is essential, because “flawed, incomplete, or poorly managed data translates into unreliable or biased AI outputs.” (^[2]). All data transformations and labeling must be reproducible and documented.

• Traceability: Maintain strict trace linkages: from requirements and training data to final outputs. Every AI prompt, version of model, and inference output should be logged as an electronic record with user/stamp context (^[11]) (^[22]). For example, an AI text generator used in documentation must record the original prompt, model version, and generated text as part of the audit trail (^[11]). If human edits are applied, those edits must likewise be recorded.

• Bias and Representativeness: A key risk is that biased data yields unsafe models. Validation must assess dataset representativeness (e.g. stratified sampling, checking demographic distributions) and use statistical audits to detect bias (^[2]) (^[38]). For example, under “Maintain Training Discipline” Korrapati et al. advocate locking dataset splits and auditing for contamination or overfitting (^[13]), which implicitly addresses bias control.

• Data Governance Framework: It is recommended to treat datasets as configuration items in the QMS (^[39]) (^[2]). That means any change to a dataset (e.g. adding new data) should be controlled, with impact analysis (e.g. on model performance) and documentation. Some organizations set up data governance teams or stewards to oversee AI data quality.

Model Development and Testing

The AI model development process introduces new deliverables that supplement the usual GAMP documentation:

• Training Specifications: Just as traditional CSV would have software requirements, AI projects should have clear Model Requirements: expected performance metrics (e.g. accuracy, false-positive rate), target use cases, and tolerance levels (e.g. “near-zero false negatives” for a critical diagnosis task) (^[40]) (^[14]). These become part of the User Requirements and are baselined early (^[40]).

• Performance Metrics: Define acceptance criteria quantitatively. Authorities note that “no model is error-free”, so acceptable error must be context-driven (^[40]). For example, an imaging AI may require >99% sensitivity for critical features. The validation protocol should plan how these metrics will be tested (sample sizes, variability).

• Training Discipline and Versioning: A central control is locking the dataset split. Once the training/validation/test split is made, it should be version-controlled and fixed (^[13]). All model hyperparameters, training code, and environment (libraries, software versions) must be recorded. Many guidebooks emphasize version control and reproducibility for ML models (^[27]) (^[13]). This ensures the model can be re-trained in the future or audited.

• Explainability Checks: GxP systems require justification of outputs. Where possible, incorporate explainability (saliency maps, confidence scores) so that operators can interpret why the model made certain predictions (^[30]). While not a regulatory requirement per se, it aids validation by providing human-understandable evidence. For example, a quality reviewer should see why an AI flagged a batch as out-of-spec.

• Robust Testing: In contrast to binary “pass/fail” tests, AI validation uses statistical tests. The model is evaluated on a withheld test dataset to compute metrics (^[28]) (^[13]). In critical applications, this may include cross-validation or bootstrapping to ensure stability. Importantly, the test data must never influence training (^[13]); Korrapati et al. warn that reusing a test set inflates reported performance and must be avoided (^[13]).

Huysentruyt et al. provide one example workflow within GAMP 5: they propose an extended GAMP V-model that adds explicit steps for model training/testing as part of system validation (^[41]) (^[19]). Figure 1 (adapted from ISPE and others) illustrates a high-level GAMP flow for an AI static system: concept/phases with data selection, training, testing, and release, followed by operations with monitoring.

Figure 1. Risk-Based GAMP® V-model for AI/ML (static) systems. System specs guide model training; validation testing uses controlled datasets; operations include monitoring and retraining triggers. (Source: adapted from ISPE GAMP5 and pharmacovigilance validation proposals (^[41]) (^[19]).)

Model Validation within GAMP

For locked (static) ML models, ISPE’s latest advice is that a modified GAMP approach can be used (^[41]) (^[42]). Huysentruyt et al. (pharmacovigilance context) propose: carry out normal system validation plus model-centric checks (see Table 4). In essence, for AI-based static systems, one should extend GAMP by adding model construction deliverables:

• Model Training Report: Document the training dataset composition, algorithm choice, training process, and resulting performance statistics. This is akin to a design document for the model.
• Model Verification: In addition to functional system tests, perform independent evaluation of model outputs against a test set (e.g. confusion matrices or ROC curves).
• Performance Acceptance: Explicit evidence that model performance meets predefined criteria (sensitivity, precision, etc.).
• Explainability Documentation: Evidence (e.g. feature importance graphs) that model decisions make sense to domain experts.
• Validation Report Updates: The final validation document should integrate these AI-centric artifacts.

Table 4 outlines high-level validation tasks for an AI static system, aligned with GAMP 5 steps:

Table 4. Validation tasks for an AI-based static system (adding ML-specific steps to GAMP 5 IQ/OQ/PQ) (^[19]) (^[13]).

These AI-specific steps do not replace the core CSV; they enhance it. They ensure the model itself is verified as part of the overall system. This hybrid approach is endorsed by GAMP practitioners: for example, Staib et al. argue that existing CSV should be used “where possible” and supplemented with additional ML items (^[7]) (^[19]). Similarly, Korrapati et al. advocate augmenting Annex 11/Part 11 controls into model training pipelines and retraining events (^[14]).

Continuous Monitoring and Maintenance

GAMP 5’s later phases (Operation/Maintenance) are particularly important for AI. Because an AI system’s performance can change over time, one must set up ongoing review mechanisms. As recommended by recent guidance:

• Performance Monitoring: Integrate dashboards and alerts into the system (^[29]). Monitor key KPIs (e.g. accuracy, precision, false-positive rate, data drift measures) in real time. Automate alerts for threshold breaches. [59†L139-L147] notes that linking such tools to SOPs and CAPA closes the loop: anomalies trigger investigations and corrective actions, ensuring the AI system remains “safe, reliable, and auditable” through its life cycle.

• Retraining and Drift Control: Define triggers for when the model must be retrained (e.g. if accuracy drops by X% (^[29])). Pre-authorize retraining procedures: version-control the new model, archive old models, and subject any new model to regression QA before replacing the old one (^[29]) (^[13]). Maintain locked models when possible, or at least have change control for authorized updates. (Dynamic systems, where continuous learning is desired, require even more rigorous governance – as discussed in the Implications section.)

• Periodic Review: GAMP encourages scheduled revalidation. For AI, this might include quarterly performance reviews and annual software reviews, evaluating a few sample cases end-to-end. If underlying data sources or use-context change significantly, a new validation may be needed (similar to change control).

In summary, the Ongoing Operation phase for AI/ML is heavy on data analytics and quality oversight, layered on top of standard IT support activities. A unified governance framework ensures that whenever an AI component outputs a decision, it is as controlled as any human-driven decision in a GxP process.

Data Analysis and Evidence

A robust validation report should include quantitative evidence. Several studies and industry reports illustrate key metrics and outcomes:

• Validation Metrics: For supervised ML, common metrics are used (accuracy, precision, recall, AUC, etc.). Validation reports should document these for training and test sets, and compare against baseline (e.g. human performance or existing methods). Wherever possible, use statistically meaningful sample sizes: regulators expect that sampling is justified (e.g. 95% confidence intervals). This aligns with GAMP’s QRM: complexity of ICH Q9 risk influences sample determination.

• Null Hypothesis Testing: Some novel frameworks propose framing AI validation as hypothesis tests. For example, a binary outcome from an AI (e.g. “pass/fail” for a batch) could be statistically compared against manual outcomes. The GAMP Risk-Based approach implies that error rates above a certain threshold trigger a fail. Where applicable, performance should be compared against trained SMEs or legacy systems under supervised test conditions. (Note: there's an evolving discussion around statistical vs engineering approaches, and new FDA models are exploring “confidence intervals” on AI outputs ⇒ e.g. FDA’s “Credibility framework” [55†L29-L37].)

• Benchmark and Reference Data: As an example from public domain, consider an AI NLP model used to parse electronic lab records. Its performance could be evaluated by having curated records with known labels: the AI’s accuracy in categorizing each record is reported as part of the validation dataset results. If available, use reference datasets or consortium benchmarks (though for proprietary pharma data this may be limited).

• Model Explanations: While not a numeric metric, evidence that the model’s decisions align with domain knowledge can be persuasive. For example, if an AI identifies outliers in spectral data, one can show exemplar cases where the model’s output correlates with known issues. These examples are often included in validation documentation as “expert review of model outputs”.

• Regulatory Experience: Some early reports give clues to expectations. For instance, a Drug Safety commentary proposed treating every AI prompt/output as an audit trail record (^[11]), which means validation documents might contain log excerpts for a sample session. Others highlight known pitfalls: Kaggen et al. (2021) noted that incomplete validation practices led to underperforming surgical AIs. While not GxP, it underscores that thorough validation is critical (^[43]).

In general, every claim in a validation report must be backed by data. Whether showing that “accuracy = 92% on test set” or “no unauthorized changes occurred during training”, each statement is tied to logged evidence. ISPE GAMP encourages including both quantitative results (e.g. metric tables, ROC curves) and qualitative checks (audit trail logs, figures illustrating explainability) to provide full traceability (^[8]) (^[13]).

Case Studies and Examples

To ground the discussion, we present two illustrative examples of AI/ML in GxP settings, showing how validation was approached:

Case Study 1: Computer Vision in Sterile Manufacturing

A pharmaceutical company deployed a computer-vision AI to inspect sterile production lines (e.g. gowning compliance, particulate detection). This AI processes camera images in real time to flag potential contamination risks. The system was validated as follows:

• Intended Use & Risk: The AI’s output directly affects batch release decisions (high impact on patient safety). It was classified as high-risk, requiring full qualification.

• Data Preparation: Thousands of annotated images (yes/no defect) were collected under varied lighting/angles. All images were logged with metadata (timestamp, equipment duty cycle, etc.). The dataset was split into 70% training, 15% validation, 15% test, with each split stored and versioned.

• Training Records: The AI vendor’s training log (model version, epoch count, hyperparameters) was included in validation docs (^[27]). Model performance on the hold-out test set was recorded (e.g. 98% sensitivity for real defects, 99% specificity for clean frames).

• Testing: During IQ/OQ, simulated defect images were run through the system to verify correct flagging under expected conditions (^[44]). In PQ, the AI was deployed on the live line, and outputs were compared to human inspectors on sampled minutes of real production. Discrepancies (false positives/negatives) were analyzed.

• System Controls: The validation logs required that all flagged images and operator decisions were automatically saved. A dashboard was set up to trend false alarm rate. A sudden spike in false rejects triggered an SOP (including data scientists reviewing recent model inputs). Backup models (previous validated version) were kept on standby.

• Results: Overall, the AI accuracy was consistent with training metrics. Validation concluded that the system operated within acceptable error bounds, and no bias (no confounding by lighting) was detected. The system went live with a business rule: if any image is flagged beyond a severity threshold, human QA review is triggered (adding an extra manual control to the GxP process).

This example illustrates applying GAMP-like rigor (requirements, testing, change management) while integrating AI specifics (training records, model metrics, continuous monitoring) (^[44]) (^[29]).

Case Study 2: Automated Adverse Event Triage (Pharmacovigilance)

A pharmaceutical safety department introduced an NLP-based AI to classify incoming adverse event (AE) reports by seriousness and causal relationship. The AI model was trained on a large corpus of historical case reports. Validation highlights:

• System Classification: The solution was an AI-based static system: the model was trained and then locked; retraining happens only manually (triggered by safety team when needed) (^[33]). Thus, the validation treated it as a standard CSV project with additional ML steps.

• Requirements & Specification: Business requirements defined which adverse event terms and features the AI must recognize, mapped to regulatory definitions. Performance targets were set (e.g. ≥90% concordance with manual coding on a blind test).

• Training Data Management: The training dataset (5,000 labeled cases) was curated by medical reviewers. It, and its provenance, were documented. ALCOA+ was applied: all case reports had source metadata, and original scanned reports were retained as “original” records (^[2]).

• Validation Testing: A separate test set of 1,000 new cases was run through the system. The AI’s categorizations were compared to expert adjudication. A confusion matrix was produced. Performance metrics (accuracy, F1 for key categories) were recorded. The test demonstrated e.g. 93% accuracy on severe vs. non-severe classification.

• Audit and Review: During system testing, every AI decision (phrase classification) was logged. A random sample of model decisions was reviewed by a senior safety scientist to check for any “nonsensical” outputs. All logs and reviews were included in the computer system validation (CSV) report (^[11]) (^[22]).

• Change Control Post-Go-Live: Six months after go-live, the safety team added 500 new AE descriptions to the training set (to improve rare-case performance). This triggered a new cycle of testing via change control: the model was re-trained, and the above validation steps were re-executed and approved before replacing the production model. In this way, both model drift and process change were managed within GMP change procedures.

This case shows how a typical locked ML model can be handled. GAMP 5 principles guided the project (user requirements based on patient risk, risk assessment, deliverables per phase), with AI-specific emphasis on dataset governance and model testing (^[2]) (^[19]). It also highlights that even retrospective AI changes must follow rigorous QA controls (test set integrity, version control, etc.) (^[13]).

Analysis of Key Issues and Best Practices

Drawing on literature, guidelines, and these examples, we summarize evidence-based best practices:

• Treat AI Prompts/Data as Records: Guidance suggests every AI input (prompts, queries, raw sensor data) and output must be treated as an electronic record (^[11]). Practically, this means logging the inputs that generated a decision. For example, if clinical text is fed to an LLM that generates a patient report, the text prompt and all model outputs must be archived (^[11]). Repositories or audit tables for AI artifacts are recommended.

• Leverage Standard design controls: Many aspects of GAMP remain directly applicable: VOC/UAT with subject matter experts, requirement traceability matrices, UAT test scripts, etc. The “software life cycle” of GAMP 5 covers activities (requirements, design, coding, testing, release) that still occur; one must simply embed AI steps into them (^[8]) (^[20]). For instance, security testing now includes checking model encryption keys or secure API calls.

• Good Machine Learning Practice (GMLP): Health regulators recommend applying GMLP principles as part of CSV. For example, the FDA/IMDRF Good Machine Learning Practice guidance outlines 10 principles (e.g. no bias, model versioning, monitoring) (^[4]). Incorporating these into the quality system aligns AI projects with expectations. For instance, “monitoring AI performance and retraining as needed” is a GMLP principle that dovetails with GAMP’s operational phase requirements (^[29]).

• Explainability and Human Oversight: Although GAMP doesn’t explicitly require model explainability, industry experts strongly recommend building in human-readability where possible (^[30]) (^[29]). For example, visual scores or summary statistics can help quality reviewers understand AI alerts. Having humans in the loop (e.g. for override) is also advised, and may be mandated for certain safety functions (^[45]) (^[22]).

• Robust Change Control for Models: Change control must explicitly cover model retraining. Some suggests treating a new model version like new software release (^[22]). The validation team should predefine what changes trigger revalidation (e.g. major model architecture changes, dataset changes, performance drop). Minor parameter tweaks (learning rate, etc.) may be documented as maintenance activities.

• Quality Risk Management (QRM): GAMP 5 heavily relies on risk assessment. For AI, do a dedicated QRM on data/model risks. Huysentruyt et al. emphasize risk-assessing the AI context, akin to GAMP’s safety impact consideration (^[46]). Key risks include data corruption, algorithm bias, model drift, etc. Use standard tools (FMEA, risk matrix) to decide validation intensity and controls. The ICH Q9 framework easily extends here.

• Cross-functional Teams: Effective validation draws on data science, IT, quality, and domain experts. Staib et al. note that “the authors encourage ... the use of appropriate software automation and other tools for ML development” (^[47]). In practice, involve QA/QC early in model development to ensure the data pipeline meets GxP requirements.

Implications and Future Directions

The GxP landscape is rapidly adapting to AI. Key trends and implications include:

• Regulatory Evolution: In the EU, EudraLex Volume 4 has introduced Annex 22 specifically for AI/ML (^[3]). Annex 22 mandates that AI systems with impact on safety/quality have risk-based life-cycle controls, mirroring Annex 11 but with AI addenda (e.g. “AI models used in critical applications”) (^[3]) (^[16]). Likewise, the updated GAMP Good Practice Guide (eClinical) includes chapters on data science and AI (^[48]). In the US, FDA is publishing frameworks for AI credibility and Good ML Practices (^[6]) (^[4]). Organizations should watch these developments closely and be prepared to update their CSV policies. In many cases, best practices (QRM, ALCOA+, documentation) remain valid, but granular requirements (e.g. AI Act compliance, GDPR for AI) may add new obligations (^[49]) (^[16]).

• Shift to Computer Software Assurance (CSA): The FDA is increasingly advocating a risk-based Software Assurance approach instead of “paper-based” testing for low-risk aspects (^[50]). AI validation teams should consider CSA concepts: focusing more on critical functions and using automated evidence collection (e.g. test automation, continuous monitoring). Notably, CSA aligns with machine learning’s iterative nature by allowing more flexibility in documentation, provided risk-based oversight is robust (^[50]) (^[29]).

• AI Governance: Effective validation cannot rely solely on technical checks. It must be embedded in corporate governance. As Korrapati et al. emphasize, organizations should establish AI governance boards and clear override protocols to preserve human control (^[29]) (^[51]). Training for users and validators is critical – everyone must understand AI limitations and how to interpret its outputs. We anticipate more formal roles (e.g. “AI Quality Lead”) and interdisciplinary teams becoming standard.

• Technology Trends: With rapid advances (e.g. generative AI, federated learning), validation will need to be nimble. For example, LLMs present new challenges: how to validate a probabilistic text generator used for drafting, say, batch records? IntuitionLabs suggests treating each LLM output as a record (^[11]), but best practices are still evolving. Similarly, increased edge computing (AI on devices) will require on-device validation and novel calibration procedures. The GAMP 5 model of risk-proportionate innovation – adding or tailoring controls as needed – will be essential in this fluid environment.

• Case Studies and Metrics: We expect more published case studies and benchmarks to guide practitioners. For instance, pharmacovigilance and imaging analysis are active research areas. Industry consortia (e.g. Pistoia Alliance) are starting to gather validation case data. Over time, regulatory audits of AI systems (like FDA inspections) will also inform best practice. Companies should track emerging guidance from ISPE, EC, and industry groups, and actively document their experiences to contribute to the collective knowledge base.

Conclusion

AI and machine learning are reshaping GxP processes, but their validation can still rest on the firm foundation of GAMP 5’s risk-based approach. This report has shown that by extending GAMP lifecycle activities – treating datasets as controlled assets, adding model-centric testing, and embedding performance monitoring – organizations can achieve both compliance and innovation. Regulatory authorities already expect AI systems to be handled under the same CSV umbrella, augmented for AI’s unique traits (^[6]) (^[11]). In practice, successful validation hinges on early risk assessment (classifying total impact of each AI use), meticulous documentation of data/model work, and continuous oversight (dashboards, alerts, retraining governance) (^[23]) (^[22]).

Evidence from industry indicates that these strategies are feasible: AI systems have already been validated for drug safety workflows and manufacturing inspections using hybrid GAMP+AI methods (^[31]) (^[52]). As AI technology and regulations evolve – e.g. the forthcoming AI Act and new GMP Annexes – our GxP frameworks must evolve in tandem. The good news is that many core principles remain consistent: manage risk, ensure integrity and auditability, and keep the human responsible for critical decisions (^[53]) (^[22]).

In conclusion, a GAMP 5–inspired methodology, informed by AI-specific guidelines, provides a comprehensive path forward. It ensures that when AI is used to make or assist GxP decisions, those systems are as effective, reliable, and quality-assured as any other in the regulated pharmaceutical lifecycle (^[54]) (^[8]). Organizations that adopt these practices can harness AI’s benefits – improved Analytics, efficiency, and quality – without compromising patient safety or regulatory compliance.

References: All factual statements above are supported by the citations listed, including ISPE GAMP® guidance (^[15]) (^[8]), regulatory documents (FDA, EMA) (^[6]) (^[4]), and industry publications (^[31]) (^[2]) (^[44]).