The FDA’s first draft guidance on artificial intelligence in drug and biologic development, released in January 2025, marked a pivotal moment for pharmacovigilance professionals. As the industry begins to interpret and apply this framework, questions remain about how AI can be safely and effectively integrated into safety monitoring processes. The guidance introduces a structured approach, but its practical application is still evolving.

Navigating the FDA’s AI framework

The FDA’s guidance centers on a risk-based credibility model. This means the level of scrutiny applied to an AI system depends on how influential it is in regulatory decision-making. If AI is simply assisting rather than making decisions, the validation requirements are less clear. Pharmacovigilance teams are grappling with questions like:

• What constitutes risk in AI-assisted safety monitoring?
• How much evidence is enough to validate AI tools?
• How should multi-purpose AI tools be documented for regulatory use?

The concept of “context of use” adds another layer of complexity. AI tools often serve multiple functions, such as case triage, literature surveillance, and signal detection. Defining a single context of use for such versatile tools is challenging.

Transparency is another key requirement. Sponsors must disclose the data used to train AI models, their performance metrics, and governance structures. However, many AI tools are developed by third-party vendors, making it difficult to access and audit this information. Pharmacovigilance teams must determine what to request from vendors to meet regulatory expectations.

Continuous learning in AI models also raises questions. As models evolve with new data and expert input, it becomes difficult to distinguish between minor updates and significant changes that require revalidation. Establishing clear thresholds for regulatory reporting is essential.

Finally, global regulatory overlap adds to the complexity. With frameworks emerging from the FDA, ICH, EU AI Act, and others, companies must decide whether to follow multiple playbooks or advocate for a unified global approach.

The promise of AI in pharmacovigilance

Despite these challenges, the potential benefits of AI in pharmacovigilance are substantial. If the FDA’s framework becomes more practical and prescriptive, AI tools could be deployed more widely, enhancing speed, scale, and precision.

Key advantages include:

• Rapid processing of case reports, literature, and social media data.
• Smarter signal detection through machine learning models that identify subtle patterns.
• Reduced manual workload by automating tasks like duplicate detection and narrative generation.
• Improved global surveillance using natural language processing across languages and geographies.
• Predictive insights that forecast safety risks before they escalate.

These capabilities can transform pharmacovigilance from a reactive function to a proactive one, improving patient outcomes and enabling more strategic risk management.

The need for specificity in guidance

For AI to be effectively integrated into pharmacovigilance, regulatory guidance must move from general principles to specific standards. Terms like “risk-based” and “transparency” need clear definitions and measurable criteria. Without this specificity, confusion will persist across areas such as model validation, context of use, and global harmonization.

Industry feedback is helping shape future iterations of the guidance. The goal is to establish norms that are universally understood and applied, reducing ambiguity and enabling consistent implementation.

The evolving role of the emerging drug safety technology program

The FDA’s Emerging Drug Safety Technology Program (EDSTP), launched in 2024, continues to support AI adoption in pharmacovigilance. While no major structural changes have been announced under new leadership, the program remains active and collaborative.

Key developments include:

• Continued focus on AI in post-market surveillance.
• Increased participation in EDSTP meetings by pharma companies, CROs, and AI vendors.
• Emphasis on mutual learning and non-binding discussions.
• Speculation that the program may evolve into a formal regulatory sandbox.

This collaborative approach is helping reduce regulatory friction and accelerate the adoption of AI technologies.

Global perspectives on AI in safety monitoring

AI adoption in pharmacovigilance varies by region, shaped by local regulatory environments, resources, and cultural factors.

• United States: Focused on innovation with oversight, emphasizing risk-based validation and transparency.
• Europe: Prioritizes ethics, explainability, and data privacy, with slower adoption due to regulatory complexity.
• United Kingdom: Encourages AI use through regulatory sandboxes and public-private partnerships.
• Japan: Takes a cautious, step-by-step approach with strong emphasis on patient safety and regulatory clarity.
• India: Rapidly scaling automation in outsourced case processing.

While each region follows its own path, the global trend is toward greater governance and harmonization in AI use.

Expanding access in underserved regions

AI has the potential to bridge gaps in pharmacovigilance infrastructure, especially in underdeveloped countries. Limited access to electronic health records, structured reporting, and internet connectivity can be mitigated through AI-enabled tools.

Examples include:

• Extracting safety data from handwritten notes and local languages.
• Supporting SMS-based or offline mobile health systems.
• Training AI models to interpret regional dialects and cultural nuances.

In advanced markets, the focus is on improving data quality and streamlining high-volume workflows. In underserved regions, AI can help establish foundational data systems and expand the reach of safety monitoring.

Reimagining the pharmacovigilance workforce

As AI takes on more of the heavy lifting, pharmacovigilance professionals will shift from data processors to data strategists. Their roles will become more cross-disciplinary, involving oversight, governance, and interpretation of complex signals.

The future workforce will require hybrid skill sets, combining domain expertise with technological fluency. Upskilling and reskilling will be essential to ensure that professionals can guide AI tools and make informed decisions.

Ultimately, AI is not replacing pharmacovigilance professionals—it is empowering them. With the right frameworks and tools, the industry can detect risks faster, protect more lives, and achieve a more equitable global impact.