AI: an early warning system for patient safety

pharmafile.com | November 3, 2026 | Feature | |  AI, Cardiology, Diabetes, Diagnostics, Obesity, Pharmacy, chronic disease, chronic pain, pharmacovigilance 

By Sanjeev Sachdeva

Safeguarding patient health is the foremost responsibility of the pharmaceutical sector. While every medicine undergoes rigorous clinical evaluation before it is approved, the true safety of a drug only fully  emerges once it enters the real-world, interacting with diverse and complex patient populations, clinical settings and treatment combinations.

With the global expansion of therapies for chronic conditions such as obesity and diabetes, combined with increasing regulatory expectations, the pharmacovigilance (PV) landscape has changed. The challenge is no longer a scarcity of safety data, but extracting meaningful data from an overwhelming influx of it. The teams should track closely to identify emerging patterns as medicines move into larger population with widespread use. 

Safety teams are now tasked with deciphering an endless stream of Individual Case Safety Reports (ICSRs), sprawling electronic health records, scientific literature and unstructured real-world databases.

On the heels of the World Patient Safety Congress, which explored how innovative technologies are transforming safety monitoring, it’s clear that the industry must now turn to exploring how agentic AI can improve proactive safety intelligence while maintaining human scientific & medical judgement and governance.

Decoding data as drug usage increases 

While the advancements in chronic disease management have drastically improved disease outcome and quality of life, it has also increased the complexity of post-market safety surveillance. Patients frequently remain on multiple concomitant medications for years and decades, to manage coexisting lifestyle diseases such as diabetes, obesity, cardiovascular and kidney diseases. This growing interaction between diseases, therapies, and patient specific factors means that emerging safety risks are often multifactorial, and increasingly difficult to detect through traditional pharmacovigilance approaches alone.

An example of how modern therapies are transforming patient outcomes can be seen in the case of diabetes treatments.

Patients are increasingly treated with combinations of glucose-lowering agents, cardiovascular therapies, obesity medicines, and renal-protective treatments. At the same time, healthcare is becoming more connected through continuous glucose monitoring, digital health applications, wearable devices, and remote patient monitoring. This creates an increasingly complex safety environment. A potential safety concern may involve not only medicine and an adverse event, but also patient characteristics, treatment duration, concomitant medications, disease progression, lifestyle factors, and real-world treatment patterns. Understanding these relationships requires more than statistical analysis. It requires scientific evaluation, medical judgment, epidemiological assessment, and benefit-risk expertise.

The industry has already shown it can cope with a sharp rise in reporting volumes, most notably during the pandemic. So, the focus isn’t on whether the system can handle another popular medicine, but whether safety teams can make sense of increasingly complex information quickly enough to step in when needed. 

Shifting from task automation to early risk detection 

Historically, PV has relied on a rigid, linear sequence: starting with an ICSR intake, moving to signal detection and validation, and ending with scientific assessment and regulatory action.

This process remains fundamental and indispensable. However, growing volumes of information mean that highly trained safety professionals often spend considerable effort organising, reconciling, and reviewing data before they can focus on higher-value scientific evaluation. The opportunity here is not only to process reports faster, but to help experts identify important patterns sooner.

AI is unlikely to replace PV processes, but it can enhance them. A more realistic future state is one in which AI acts as an additional analytical layer capable of identifying weak signals and emerging patterns across multiple sources of evidence.

This is where AI acts as a vital early-warning radar within the safety operating model, analysing millions of data points, highlighting anomalies and unusual patterns for medical safety review, leading to signal validation, scientific assessment and then appropriate risk-management action. 

More advanced agentic AI, where AI powered agents are engineered to execute specific tasks, extends this approach by coordinating tasks across connected workflows, with clear escalation points for human review. 

Practically, this can help biopharma companies reduce operational effort by 30-50%, accelerate regulatory reporting, identify safety signals earlier, strengthen compliance, and reduce the risk of inspection findings, product recalls, and patient safety events. 

For larger safety reports that combine many cases, AI can help pull together the relevant evidence and draft sections, while human experts still review and sign off the conclusion. This brings faster execution whilst ensuring more reliable information, which is available sooner, giving safety teams a stronger foundation for identifying what needs closer investigation.

Importantly, AI does not establish causality, validate safety signals, or make regulatory decisions. These remain the responsibility of qualified PV professionals, medical safety physicians, epidemiologists, and regulatory authorities. The value of AI lies in helping safety experts focus attention on the right questions sooner.

Real-world data, predictive insights

The next opportunity is to look beyond individual reports. Under-reporting persists, while relevant evidence may sit in electronic health records, claims data, registries, clinical studies or scientific literature.

For example, older patients who have diabetes may take a particular medicine and receive another treatment at the same time. Individual reports may not make the connection apparent. AI-supported analysis across appropriate sources could help bring that pattern to specialists’ attention sooner.

Initiatives such as the FDA Sentinel programme and the EMA’s DARWIN EU record demonstrate the importance of broader real-world evidence in safety assessment. They also highlight why AI is becoming critical: the volume and complexity of data is too great to analyse manually at speed but can reveal patterns that help specialists identify emerging safety risks earlier. This also requires collaboration, as no pharmaceutical company has a complete view of patient experience.

AI can help experts navigate that evidence, subject to appropriate access, privacy safeguards, and analytical methods. However, it’s important to remember that these underlying illnesses, concomitant medicines, and reporting biases still require scientific assessment. The aim is to identify important questions sooner, not automate the answers.

 AI Augments, PV Experts Decide

Where AI is concerned, agents can handle processes, but people must remain the first to make judgement calls. 

By taking on repetitive preparation and coordination, AI can free safety experts to investigate complex cases and determine appropriate risk-management measures. Decisions involving medical judgement and ambiguity must remain with qualified professionals.

That requires governance by design. Organisations need defined responsibilities, quality standards and escalation routes, alongside a traceable record of source information, AI outputs and human decisions. Every assessment must be capable of standing up to scrutiny.

Validation before deployment is only the beginning. Teams must monitor whether systems continue to perform as intended and intervene when output becomes unreliable. This also creates new skills requirements in data quality and AI oversight.

Human expertise becomes more valuable, not less. Reviewers need the evidence, time and authority to challenge outputs, not simply approve them.

Ultimately, the success of AI in PV must be measured by more than just reduced cycle times or operational cost savings. Its true ROI lies in its ability to empower safety teams to detect emerging risks before they escalate.

Earlier detection can translate into meaningful clinical action, whether through targeted investigations, enhanced risk minimisation measures, updates to prescribing information (PIL), or timely regulatory communications that help protect patients before safety issues become widespread. 

Technology will never entirely eliminate drug safety risks, nor can it replace the profound medical intuition required to assess them. Core Activities like signal validation, causality assessment, and regulatory decision-making will continue to require qualified pharmacovigilance professionals. 

However, by shifting the burden of data synthesis from humans to machines, AI acts as an indispensable digital colleague. The future of PV is not autonomous decision-making, but intelligent decision support, amplifying human expertise, accelerating insights, improving quality of surveillance and fundamentally elevating how the industry protects patients globally.

Pic: NCI

Sanjeev Sachdeva is Global Head – Advisory and BPS, at Life Sciences Tata Consultancy Services (TCS)