Artificial Intelligence in Clinical Trial Pharmacovigilance

Pharmacovigilance (PV) teams are managing growing volumes of safety data, increasingly complex clinical trial designs, and rising regulatory expectations. Artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), is now being explored as a support mechanism to enhance consistency, speed, and oversight across clinical trial safety activities.

AI does not replace medical judgement; rather, it strengthens existing processes by improving data organization and enabling clearer pattern recognition.

Date: 17 February 2026 | Ref: ART019

Practical Applications of AI in Clinical Trial PV

Domain	Key Point
Adverse Event Detection and Case Processing	AI tools can extract events from unstructured text, flag missing or inconsistent information, identify seriousness indicators, and support early case triage. Outcome: Faster and more consistent case handling.
Signal Management Support	AI helps group similar events, identify emerging trends across sites or treatment arms, and detect duplicates. Outcome: Betterinformed and more structured safety review discussions.
Risk Assessment and Ongoing Oversight	AI can surface recurrence patterns, severity predictors, and highrisk patient subsets. Outcome: More focused monitoring and timely benefit–risk evaluation. Explainability remains essential so clinicians understand why the model highlights particular concerns.
Regulatory Reporting Readiness	AI can flag missing fields, identify coding mismatches, and support dataquality checks. Outcome: Reduced reporting delays and improved submission quality.

Governance, Validation, and Regulatory Considerations

AI systems used in pharmacovigilance must operate within a clearly defined and validated governance framework to ensure regulatorycompliant safety activities. Each AI tool requires a wellspecified intended use, documented validation demonstrating suitability and reliability, and ongoing performance monitoring to ensure outputs remain accurate as data and study conditions evolve.

Human oversight must remain central, with qualified safety professionals responsible for interpreting AIgenerated insights and making final decisions. Transparency is equally critical, including clarity about training data, model limitations, and how outputs are produced. Regulators expect sponsors to retain full accountability for all pharmacovigilance activities supported by AI, whether implemented directly or through CRO partners.

Ethical Considerations and Data Protection

The use of AI does not alter obligations related to patient confidentiality, data integrity, or the ethical management of safety information. Organizations must ensure appropriate handling of personal data, monitor algorithmic bias, and evaluate the accuracy and reliability of AI outputs. Clear escalation pathways for potential safety concerns and sustained clinician involvement are essential, as trust depends on transparent processes and disciplined oversight.

The Future of AI in Clinical Trial PV

Adoption of AI in clinical trial pharmacovigilance is expected to remain deliberate and riskbased, with organizations integrating technology only where it demonstrably strengthens safety oversight. AI will likely provide increasing value by improving efficiency in dataheavy workflows, enhancing consistency across case processing, supporting earlier recognition of emerging patterns, and reinforcing inspection readiness through more reliable data organization and quality controls.