The field of pharmacovigilance faces a constant stress of time, compliance, and quality. The level of stress over the years has changed significantly. From around 700,000 per year of ICSRs received by the FDA in 2010, there have been over 2.1 million in recent times. These reports come from various sources including hospitals, applications, social media, and devices, in several languages. The pressure exerted by the regulator, in contrast, has not reduced but increased.
Industry data from the Indegene 2023 PV Industry Survey confirms where leadership focus sits:
- 40% prioritize regulatory compliance [approx.]
- 30% prioritize quality [approx.]
- 29% prioritize operational efficiency [approx.]
Compliance and quality are the mandate. Everything else is secondary.
In this blog, we are unlocking how AI is solving the challenges in ICSR submissions and improving compliance and quality across PV operations.
What effect does AI have on quality and compliance in pharmacovigilance?
AI in pharmacovigilance adds value in areas such as those at which variation occurs and from which the audit findings stem.
Automated Case intake & standardization
AI streamlines the earliest stage of ICSR processing by automatically extracting and validating information from multiple sources.
Key capabilities include:
- Extracting data from emails, PDFs, E2B XML, fax, and other unstructured documents
- Validating cases against the four minimum ICSR reporting criteria
- Identifying serious cases early in the workflow
- Reducing missing information and follow-up requests
- Minimizing downstream rework and submission delays
MedDRA coding automation
Consistent coding is critical for inspection readiness and reliable safety data.
AI helps organizations:
- Reduce coding variability across processors
- Improve MedDRA coding consistency
- Build standardized safety datasets
- Reduce coding inconsistencies that may arise during inspections
Industry Insight: Only 5% of companies have extensively adopted artificial intelligence for MedDRA coding, as per the Indegene 2023 survey.
Structural Case narrative building
Preparing case narratives is one of the most time-consuming activities in PV operations.
AI supports processors by:
- Generating structured first-draft narratives
- Standardizing documentation quality
- Reducing review effort
- Accelerating case completion
- Improving consistency across high-volume processing
Regulatory submission automation
AI enables straight-through processing for routine post-marketing cases by automating several repetitive activities.
Typical automation includes:
- Case ingestion
- Validation
- Duplicate detection
- Workflow routing
- Submission preparation
- Case closure
For standard non-serious cases, AI-powered workflows can automate 50–60% of end-to-end processing, allowing pharmacovigilance teams to focus on complex safety reviews and risk management.
Signal detection and risk assessment
AI strengthens signal detection by analyzing data across multiple safety sources.
It supports earlier identification of safety signals by combining:
- Spontaneous reporting systems
- Electronic Health Records (EHRs)
- Patient-generated data
- Real-world evidence
This enables faster identification of rare or emerging safety patterns while reducing reporting delays.
One-of the largest global pharma gaints improved compliance and product quality, leading to higher pharmacovigilance scores with AI-powered data analytics.
What the FDA Is Now Formally Requiring
FDA released its draft guidance on “Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products” in January 2025.
The above mentioned guidance is one of its kind as it deals with the use of artificial intelligence during the lifecycle of drugs and biological products.
Organizations using AI for safety assessments, ICSR processing, or signal detection should be able to demonstrate:
- Intended use of the AI model
- Validation methodology
- Performance metrics
- Known limitations
- Human review process
- Audit-ready documentation
Validation requirements increase with the level of regulatory impact. AI supporting internal workflows requires less evidence than AI influencing labeling, safety decisions, or regulatory submissions.
The framework is based on more than 500 AI-enabled regulatory submissions reviewed by the FDA between 2016 and 2023.
Table of Contents
Why human validation is essential for AI in pharmacovigilance?
Dr. Robert Ball, Deputy Director of the Office of Surveillance and Epidemiology at the Center for Drug Evaluation and Research of FDA, on FDA podcast on AI in pharmacovigilance, said directly that AI will be able to facilitate processes like identification of cases needed for reporting and event seriousness determination but still human validation will be absolutely necessary especially in causality evaluation as the human expert opinion cannot be replicated by AI now.
Human review checkpoint should be incorporated into every use case of AI in PV along with the opportunity for overriding the process and performance monitoring after implementation.
Conclusion
AI in pharmacovigilance is most valuable when applied precisely, at the structural sources of variability that drive compliance risk and quality findings. The FDA has now formalised the framework for how that application must be documented and governed.
Building validated & auditable AI workflows ahead of finalised guidance with human oversight embedded by design enables a regulated and complaint PV process. Saxon’s AI solutions for pharma industry encloses use cases that helps teams build compliant, auditable, AI-powered safety workflows.
Frequently asked questions
The term pharmacovigilance AI means the application of technologies like machine learning, natural language processing, and automation in pharmacovigilance processes, such as ICSR intake and processing, MedDRA coding, story generation, signal detection, and regulatory reporting. Using this technology, one will be able to improve the effectiveness of all those processes.
AI automates intake from multiple source types (email, PDF, E2B XML), validates cases against minimum reporting criteria, checks for duplicates, and routes cases based on seriousness — before a human processor is involved. This reduces manual data entry errors, prevents missing fields from propagating downstream, and enables straight-through processing for 50–60% of standard non-serious cases, freeing trained staff for complex work.
The FDA published its initial guidance for the use of AI in drug regulation decision-making in January 2025. This sets out a credibility framework on the basis of risk for the use of AI in submissions. AI technologies which are applied in pharmacovigilance activities that contribute to regulatory decisions must be validated through documentation. It is the position of the FDA that AI complements human judgment.
Some of the challenges firms may face include high investment costs, difficulties in integrating existing machinery, security threats, and lack of skilled manpower.
The risks are: poor validation of outputs by AI before implementation in regulatory processes, lack of sufficient human oversight in making important decisions such as causality determination, lack of traceability and documentation of reasoning behind the results, and variability of performance in different case studies. These risks can be mitigated with proper governance through a credibility assessment process according to FDA guidelines.
No. The FDA has explicitly stated that human expertise remains essential — particularly for causality assessment, where current AI models cannot replicate the nuanced judgment of experienced safety professionals. AI is most effective when it handles high-volume, consistency-sensitive tasks, allowing PV professionals to focus on complex cases, signal analysis, and risk management decisions.
The EDSTP, launched in 2024, is an FDA program designed to facilitate the responsible adoption of AI and emerging technologies in pharmacovigilance. It provides a collaborative channel for industry to engage with CDER on questions related to AI tool validation, implementation, and regulatory expectations — before those tools are deployed in submissions.
Assess the vendors on the following four points: (1) if their tools are validated and documented in accordance with the requirement for the level of credibility framework of FDA based on risk-based credibility; (2) if they can provide human review check-points and audit trail; (3) if they have proven data of performance in a similar PV environment; and (4) if their implementation includes performance monitoring.