How AI is Transforming Signal Detection and Medical Review

The pharmacovigilance (PV) landscape is rapidly evolving — and artificial intelligence (AI) is at the forefront of this transformation. As pharmaceutical companies, biotech firms, and regulatory authorities navigate increasing data volumes and rising complexity, AI offers unprecedented opportunities to improve signal detection and medical review processes.

At its core, pharmacovigilance is about ensuring patient safety by identifying, assessing adverse reactions, and preventing potential future adverse drug reactions from causing harm in the patient population for which the drug is indicated. But traditional PV workflows, often reliant on manual review of case reports, literature articles, and clinical safety databases, are increasingly strained under the weight of growing data sources which make it difficult and time consuming to identify potential safety signals in real time.

The burning question regularly is - Where does AI come in with signal detection and medical review ?

AI enables “smarter” signal detection, particularly machine learning (ML) algorithms, which can process vast amounts of structured and unstructured data at speeds far beyond human capability. By recognizing subtle patterns and trends, AI tools can help detect emerging safety signals earlier, prioritize them more effectively, and reduce the risk of missing important safety concerns. This not only improves patient safety but also results in a smarter, more responsive PV system that aligns with the industry's mission to safeguard public health.

AI can enhance Medical Review, where AI-driven natural language processing (NLP) tools can assist medical reviewers by automatically summarizing key case information, highlighting relevant medical history, and even suggesting potential causality assessments. Rather than replacing the clinical expertise of medical reviewers, AI serves as a powerful augmentation tool — streamlining case review, reducing repetitive tasks, and allowing the medical & safety experts to focus on complex judgments that require human insight. This synergy between human insight and machine intelligence leads to more efficient, consistent, and high-quality safety assessments.

AI’s integration can evolve the classic PV strategy from reactive to proactive, where pharmacovigilance teams can shift from a reactive stance to a more proactive approach by utilizing AI enabled predictive models which can help anticipate safety issues before they escalate, while automation can free up resources to deepen scientific analysis and strategic risk management.

What does this mean for the current PV industry?

For companies seeking a competitive edge, leveraging AI in PV is no longer a futuristic vision — it’s a present-day inevitability. Organizations that embrace AI will be better positioned to manage risk, maintain compliance, and ultimately deliver safer therapies to patients worldwide.

At Clinigen we help clients navigate this evolving PV landscape by providing strategic advice, technology assessments, and tailored solutions that starts the integration of AI into their pharmacovigilance operations. Whether you are just starting to explore AI or looking to optimize existing systems, we can help you harness the full potential of these transformative technologies.

Let’s re-shape the future of pharmacovigilance together.

References:

1. U.S. Food and Drug Administration (FDA). Artificial Intelligence and Machine Learning (AI/ML) in Drug Development and Use Discussion Paper. https://www.fda.gov
2. U.S. Food and Drug Administration (FDA). Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological
3. European Medicines Agency (EMA). Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle. https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-use-artificial-intelligence-ai-medicinal-product-lifecycle_en.pdf
4. International Society of Pharmacovigilance (ISoP). AI Special Interest Group White Papers. https://isoponline.org
5. Wittenstein et al. (2019). The role of artificial intelligence in pharmacovigilance: A systematic review. Drug Safety, 42(8), 921–930.
6. World Health Organization (WHO). Reporting and Learning Systems for Medication Errors: The Role of Pharmacovigilance Centres. = https://www.who.int