ecent advances in drug discovery, development, manufacturing, and safety monitoring technologies, including the adoption of automation, robotics, simulation, and other digital capabilities, have allowed sponsors and manufacturers to reduce sources of error, optimize resources, and reduce patient risk.

Moreover, artificial intelligence (AI) can streamline software validation and documentation, one of the most resource-intensive aspects of software technologies, by automatically generating traceability matrices and audit-ready reports. At the same time, the validation of AI itself raises new challenges around transparency, fairness, and reproducibility, as reflected in frameworks such as BMJ publications’ Validation framework for the use of AI in healthcare: overview of the new British standard and other initiatives like Epic’s open-source AI validation suite.

US FDA Regulatory Context

FDA recognizes the potential for these technologies to provide significant benefits for enhancing the quality, availability, and safety of medical devices and has undertaken several efforts to help foster the adoption and use of such technologies. Regulatory oversight of computerized systems validation was first established with the Good Laboratory Practice (GLP) regulations in 1978, expanded by the FDA’s Guidance on Process Validation in 1987, and revised in 2011 (Revision 1). Although these regulations and guidance documents did not specify how validation should be performed or documented, regulated organizations quickly adopted a documentation-heavy approach, often including screenshots during testing as evidence of validation. This practice has remained largely unchanged for decades, despite the introduction of new guidelines promoting a risk-based approach to validation, such as FDA’s General Principles of Software Validation, 21 CFR Part 11 Scope and Application, and, more recently, FDA’s Final Guidance on Computer Software Assurance for Production and Quality System Software (CSA). (For more information, see General Validation Requirements at the end of this article.)

The Potential of AI in Computer Software Validation

With the continuous growth of Software as a Service (SaaS) and the fast evolution of AI, industry can begin to unlock more effective ways to validate software and develop adequate validation deliverables. AI is rapidly reshaping computerized systems validation (CSV) by moving away from rigid, documentation-heavy methods and embracing more flexible, risk-based assessment approaches. AI enhances this shift by automating key validation activities such as risk assessment, test script generation and execution, continuous validation, and ongoing system monitoring. By applying machine learning (ML) algorithms, AI has the potential to proactively identify high-risk areas, evaluate the impact of software changes, and improve compliance with regulatory requirements through increased precision and efficiency.

Potential CSV Use Cases Using AI

As the full AI potential in CSV keeps evolving, more and more possible use cases keep emerging, including the hypothetical examples below.

1. Automate Documentation Development, Improve Documentation and Audit Readiness
   AI can generate first drafts of validation plans, summary reports, and other required records using pre-defined templates aligned with regulations and guidelines. Additionally, it can go through the regulations and specific databases to compile the first draft of user requirements specification ready to be reviewed by Subject Matter Experts (SMEs) and Quality Assurance Professionals (QAPs). It can also compile checklists of necessary validation documents based on software type and applicable standards. Other functionalities could include reviewing validation documents and populating summary reports in standardized templates. Large software developers (e.g., for electronic case report forms [eCRFs] or electronic Trial Master Files [eTMFs])—are building AI-driven databases of typical user requirements across therapeutic areas, which SMEs, QAPs, and pharmaceutical and biotechnology clients may customize.

   Documentation has been considered one of the most resource-intensive aspects of validation. AI can streamline this process by automatically capturing test evidence, generating traceability matrices, and compiling audit-ready reports. These capabilities would significantly reduce administrative burden while maintaining compliance with FDA and EMA requirements for data integrity, traceability, and inspection readiness.

2. Generate Traceability Matrices, Test Plans, and Test Scripts
   A requirements traceability matrix is built to ensure all requirements for a computerized system have been adequately tested and validated by linking system user requirements to test scripts documents. AI can identify missed requirements faster than humans and automate the creation of trace matrices. It can also generate test plans and propose positive and negative test cases, likely saving time and improving coverage of all requirements.
3. Create Synthetic Test Data
   One promising application of AI in validation is the generation of synthetic test data, which can reduce reliance on historical cases and accelerate coverage of new scenarios. Traditional test data sets are often limited, time-consuming to build, and fail to capture evolving workflows or rare events. AI-driven synthetic data generation may offer a faster, privacy-preserving alternative by creating diverse, clinically plausible records that can be tailored to high-risk validation needs. To be effective, AI assistants must be trained to recognize “successful examples” and derive meaningful analogies, enabling tests that generalize to real-world use. Beyond individual organizations, collaborative sharing of synthetic data sets could enrich validation efforts across the industry, broadening variety and strengthening robustness. Importantly, synthetic data sets can also draw on real-world data to surface new insights into disease patterns, therapy safety, and unmet medical needs, helping validation evolve in step with clinical innovation while maintaining compliance and privacy.

   Synthetic data is being widely used in many use cases (especially synthetic data based on real-world data in drug development) but is not yet being used in CSV. The assumption is that the industry is not very far behind on this.

4. Detect Anomalies in Test Results
   AI can further enhance validation by identifying anomalies in system behavior and test results. Just as AI has been used to detect protocol deviations in clinical trials (see sample company news announcements here, here, and here), it can evaluate audit trails and logs to uncover both common and unusual patterns. By flagging inconsistencies or deviations, AI would support human reviewers to identify areas of potential risk. While such features will themselves require validation, they hold strong potential for reducing error rates and focusing oversight where it matters most.
5. Predictively Manage Risk
   In addition to anomaly detection, AI can support predictive risk management by analyzing historical validation data and audit reports. With the right training grounded in regulatory guidance and risk-based validation literature, AI can build models that forecast high-risk areas, evaluate the effectiveness of past mitigations, and propose streamlined deliverables such as validation plans, test scripts, and summary reports. More importantly, AI can adjust risk levels dynamically based on system characteristics such as electronic record criticality, computerized system functionality, SaaS deployment, or cloud provider attributes, enabling validation to be tailored precisely to the environment.
6. Track User Compliance and Training
   AI can strengthen compliance by managing training obligations. By generating role-based training matrices, monitoring completion, and issuing reminders, AI would ensure that staff remain up to date with validation requirements. In high-risk environments, AI can intelligently restrict user access until training is completed, reducing audit findings and improving compliance and giving assurance that systems are only operated by qualified personnel.

Validating AI-Enabled Clinical Applications

Clinical AI applications introduce unique challenges related to transparency, bias, and reproducibility. New standards such as BS30440 (2023) establish auditable frameworks for AI in healthcare, requiring evidence of safety, equity, and integration into clinical workflows. At the same time, industry initiatives like Epic’s open-source AI validation suite demonstrate practical ways to locally test and monitor AI models integrated into EHRs. These developments illustrate how validation practices are adapting to emerging technologies while ensuring patient safety and regulatory compliance.

Benefits and Risks of Implementing AI Tools in Regulated Industry

In regulated industries, the successful use of AI requires systematic risk identification, mitigation strategies, and continuous human oversight (i.e., the Human in the Loop). When properly managed, AI can enhance compliance, safeguard trial participants, improve product quality, and strengthen the reliability of data used in regulatory and organizational decision-making. The deployment of AI tools and assistants should be guided by careful evaluation to ensure that benefits are realized while risks are controlled or managed.

AI tools offer several potential benefits in computerized system validation and software development, including:

• Recommending fit-for-purpose approaches through rapid analysis of historical data.
• Identifying high-risk system requirements and functions and detecting software changes earlier hence lowering overall development costs; and lowering production costs for widely used software systems (once the process has proven successful) to increase accessibility for smaller organizations.
• Automating risk assessments and generating initial documentation, creating and optimizing test scripts, detecting potential anomalies in validation outputs.
• Supporting continuous validation and self-validation by operating around-the-clock (“24/7”), improving compliance and reducing deployment risks.
• Enabling seamless integration of AI with existing software tools.

Despite these benefits, the potential risks must not be underestimated, particularly in regulated environments where inadequate controls or unauthorized AI use can have serious consequences, such as:

• Introducing bias due to insufficient or unrepresentative training data sets or drifting model parameters resulting in unreliable outputs and nonfitting recommendations.
• Breaching confidentiality during data collection or training or leaking sensitive information.
• Using insecure coding practices, leaving systems vulnerable to cyberattacks by hackers and malicious actors.
• Lacking developer training, resulting in inadequately tested AI models.

The deployment of AI in CSV introduces its own risks and potential mitigations that must be evaluated independently of traditional CSV risk assessment. The table below presents some key considerations related to potential risks to patient and product safety and their mitigation.

Legend:
QAP – Quality Assurance Professional
SME – Subject Matter Expert

Legend:
QAP – Quality Assurance Professional
SME – Subject Matter Expert

Conclusion and Future Perspectives

Since the FDA issued its guideline on computerized system validation, the field has shifted from rigid, documentation-heavy practices toward risk-based and assurance-driven frameworks. The introduction of computer software assurance (CSA) reflects this evolution, encouraging proportional testing, critical thinking, and flexibility. Within this landscape, AI may emerge as both a powerful enabler of validation and a novel subject requiring validation itself.

By automating repetitive tasks, intelligently prioritizing risks, enabling adaptive validation cycles, and supporting audit readiness, AI can directly reinforce the principles of computer software assurance. At the same time, AI-enabled validation tools provide a framework to evaluate emerging technologies such as ML algorithms and large language models, which do not fit into legacy CSV methodologies.

AI-driven tools may have the potential to automate test generation, enable self-healing test scripts, and prioritize validation efforts according to risk. They also support continuous and adaptive validation, particularly valuable for established and rapidly evolving SaaS or ML-based systems.

There are several directions that will shape the future of AI-enabled validation in healthcare:

• Regulatory harmonization across FDA, EMA, and MHRA to address continuous validation of adaptive systems leading to integration of AI assurance tools into GxP compliance workflows spanning GCP, GMP, and GLP.
• Evolving models of human–AI collaboration that balance automation with expert oversight.
• Development of ethical and equity-focused validation frameworks to mitigate bias and ensure fairness in clinical AI applications and cross-industry collaboration and adoption of open-source validation tools to establish globally recognized best practices.

AI offers the (still mostly unrealized) opportunity to transform validation from a retrospective compliance exercise into a strategic enabler of safe and efficient healthcare innovation. By embedding intelligence into validation processes while also holding AI systems themselves to rigorous standards, the industry can achieve a future where compliance, agility, and patient safety are not competing priorities but integrated outcomes. As both regulators and industry stakeholders adopt AI-driven validation frameworks, the balance between efficiency and innovation will become more achievable, making AI not only beneficial but an essential element for future software validation.