Is AI The Key To Earlier Detection Of Pharmaceutical Safety Risks?

October 8, 2026

Contributed Commentary by Sanjeev Sachdeva, Tata Consultancy Services (TCS)  

October 8, 2026 | Patient safety has always been the foundation of pharmaceutical development. While medicines undergo rigorous evaluation throughout clinical development, their benefit-risk profile continues to evolve once they are used across patient populations. 

This post-marketing phase generates an unprecedented volume of safety data. Pharmacovigilance teams must continuously evaluate evidence from Individual Case Safety Reports (ICSRs), clinical studies, scientific literature, electronic health records, patient registries, and real-world data sources. The challenge is no longer data collection, but the timely detection and interpretation of clinically meaningful safety signals amid increasing data complexity. 

As therapeutic landscapes expand in areas such as diabetes, cardiovascular disease and oncology, the need for earlier identification of emerging risks becomes more critical. This raises an important question: how can artificial intelligence help transform vast and fragmented safety data into actionable insights while preserving the rigorous clinical judgment, regulatory oversight, and evidence-based decision-making that underpin patient safety? 

AI offers the potential to augment pharmacovigilance by accelerating signal detection, evidence synthesis, and risk assessment. However, its value doesn’t lie in replacing scientific expertise, but in enabling safety professionals to identify potential risks sooner, evaluate them more comprehensively, and support proactive protection of patients. 

Connecting The Dots In A High-Volume World 

The expansion of chronic therapies has improved patient outcomes, but it also makes post-market surveillance more complex. Patients stay on treatments longer, often managing multiple conditions with combination drug regimens. 

Recent events highlight this challenge. This year, 11,460 bottles of the blood pressure medication chlorthalidone were recalled after failing dissolution specifications, representing a clear product-quality issue. Further, a European Medicines Agency (EMA) review of semaglutide medicines identified a rare eye condition as a potential side effect, leading to updated product labels across Europe. 

While one issue involved manufacturing consistency and the other involved a systemic adverse reaction, both demonstrate that effective safety oversight must continue throughout a medicine’s lifecycle. 

Modern diabetes care provides a clear case study, with patients often taking combinations of glucose-lowering, cardiovascular, obesity, and kidney disease therapies while generating continuous data through glucose monitors, wearables, and digital health tools. 

In this environment, a safety concern rarely involves a single drug causing an isolated side effect. Instead, it involves patient characteristics, treatment duration, concomitant medications, disease progression, and lifestyle factors. Understanding these relationships demands medical judgment, epidemiological assessment, and benefit-risk expertise. 

The pharmaceutical industry demonstrated during the COVID-19 pandemic that it could handle large spikes in reporting volumes. The real question now is whether safety teams can make sense of increasingly complex information quickly enough to step in when needed. 

Moving Beyond Processing Speed To Early Detection 

Traditional pharmacovigilance follows an established linear process: 

  • ICSRs 

    Signal Detection 

  • Signal Validation 

  • Assessment 

  • Regulatory Action 

While this sequence remains indispensable, safety professionals spend substantial effort manually gathering and reviewing data before conducting scientific analysis. The opportunity isn’t to process reports faster, but to help identify patterns sooner. 

AI can serve as an analytical early-warning layer within the workflow: 

  • Millions of Data Points  

  • AI Pattern Identification Medical Safety Review  

  • Signal Validation 

  • Scientific Assessment 

  • Risk Management 

Advanced Agentic AI can automate tasks such as data extraction, literature screening, and case collation, reducing operational effort by 30% to 50%, accelerating regulatory reporting, and lowering compliance risk. Most importantly, it gives safety teams more time to identify and investigate emerging risks sooner. 

Tapping Real-World Evidence Across The Ecosystem 

Under-reporting remains an issue, and critical evidence often resides within health system databases, registries, or academic literature. 

For example, an elderly patient with diabetes might take a specific drug alongside a secondary treatment. Individual reports may not reveal a connection, but AI-supported analysis across broader datasets can flag that pattern sooner. 

The FDA’s Sentinel Initiative and the EMA’s DARWIN EU network demonstrate the growing importance of real-world evidence. Since no single biopharma company has a complete view of the patient experience, AI can help navigate these large datasets to highlight unexpected drug interactions or sub-population risks. 

Governance By Design: Keeping Humans In Control 

AI can accelerate insight generation, but it cannot establish medical causality, validate safety signals, or make regulatory decisions. These responsibilities remain with safety physicians, scientists, epidemiologists, and regulators. 

To deploy AI responsibly, governance must be embedded by design through: 

  • Traceability with auditable links to source data 

  • Human escalation pathways for critical findings and exceptions 

  • Continuous monitoring to ensure accuracy, reliability, and regulatory compliance 

As AI adoption grows, human expertise becomes more important. Experts must retain the authority, context, and time to challenge, validate, and ultimately own safety decisions. 

Intelligent Decision Support For Better Patient Protection 

AI’s value in pharmacovigilance extends beyond efficiency by helping safety teams identify emerging risks earlier, enabling more timely signal evaluation, benefit-risk assessment, and risk mitigation. While AI can uncover patterns across vast and complex datasets, it doesn’t replace the scientific judgment needed to assess safety concerns, product quality issues, or regulatory decisions. Instead, it serves as an intelligent decision-support tool that augments human expertise, helping organizations make more informed decisions and take a more proactive approach to protecting patient safety. 

Sanjeev Sachdeva is the Global Head of Life Sciences at Tata Consultancy Services (TCS). He leads strategic initiatives helping biopharmaceutical organizations leverage advanced technologies and AI to improve clinical research, regulatory compliance, and patient safety operations worldwide. He can be reached at [email protected].