What AI Can’t Do in Pharmacovigilance and Why It Still Matters

Artificial intelligence is now embedded in pharmacovigilance workflows. It supports case intake, literature screening, duplicate detection, and even signal prioritisation. Many teams report efficiency gains and reduced manual workload. Yet despite rapid adoption, there are critical areas where AI can’t do in pharmacovigilance what human experts still must. Understanding these limitations is essential for patient safety, regulatory compliance, and sound benefit-risk decisions.

This article explores what AI can’t do in pharmacovigilance and why those gaps matter more than ever.

AI Can’t Replace Clinical Judgment in Pharmacovigilance

Pharmacovigilance decisions are rarely binary. Determining causality, medical plausibility, or clinical relevance requires judgment shaped by experience and context. AI can identify correlations across large datasets. It cannot reliably assess whether an adverse event is clinically meaningful in a specific patient scenario. Comorbidities, disease progression, and off-label use often define the real risk picture. These nuances are not consistently captured in structured data.

This is one of the most important areas where AI can’t do in pharmacovigilance what trained safety professionals do daily.

AI Can’t Understand Patient Narratives

Individual case safety reports are not just collections of fields. They are stories told by patients, caregivers, and healthcare professionals. These narratives are often incomplete, emotional, and inconsistent. AI systems can extract terms and timelines. They struggle to interpret intent, concern, or credibility. A subtle change in wording can signal a serious emerging risk to a human reader but appear insignificant to an algorithm.

Because pharmacovigilance relies heavily on narrative interpretation, this remains a clear example of what AI can’t reliably do in pharmacovigilance.

Example: Patient Narrative That Changed Case Assessment

An individual case safety report appeared routine, with structured fields. The seriousness criteria and MedDRA coding suggested a known non-serious adverse event.

During the narrative review, a safety physician noticed repeated expressions of fear and functional impact described by the patient. The wording indicated a severity far greater than reflected in the coded data.

The case was reassessed and upgraded, which impacted aggregate analysis. AI extracted the data correctly but failed to understand the clinical and emotional context. This remains one of the most common areas where AI can’t do in pharmacovigilance what human reviewers do instinctively.

AI Can’t Take Accountability for Safety Decisions

Regulators do not inspect algorithms. They inspect companies and the people within them. When a signal is missed or a risk-minimisation action is delayed, accountability rests with human decision-makers. AI can support analysis, but cannot justify decisions during inspections or health authority queries.

Ownership of safety decisions is central to pharmacovigilance. This responsibility cannot be delegated to technology, no matter how advanced it becomes.

AI Can’t Navigate Regulatory Expectations Fully

Pharmacovigilance is shaped by evolving regulatory interpretation. Guidelines define minimum standards, but real world expectations vary by region and over time. AI systems do not truly understand regulatory nuance. They cannot anticipate inspection focus or adjust interpretation based on recent enforcement trends. What is acceptable in one context may be questioned in another.

This regulatory complexity is another reason AI can’t do in pharmacovigilance what experienced professionals manage through institutional knowledge and judgment.

AI Can’t Handle Uncertainty the Way Humans Can

Safety science operates in uncertainty. Signals are weak, data is incomplete, and confounding factors are common. AI performs best when trained on stable patterns. Novel risks, rare adverse events, and emerging safety concerns often fall outside training data. In these situations, AI output may appear confident while being fundamentally unreliable.

Human experts are trained to recognise uncertainty and act cautiously. This skill remains essential in pharmacovigilance.

Why These Limitations Matter for the Future of PV

The risk is not that AI will replace pharmacovigilance professionals. The greater risk is uncritical reliance on automated outputs. When teams stop questioning results because the system produced them, safety oversight weakens. Pharmacovigilance becomes efficient but less thoughtful. Patient protection depends on maintaining human judgment at the centre of safety systems.

Understanding what AI can’t do in pharmacovigilance helps organisations design more innovative hybrid models in which technology supports expertise rather than replacing it.

Artificial Intelligence as a Tool, Not an Authority in Pharmacovigilance

AI is most valuable when it reduces noise and administrative burden. It excels at processing volume and highlighting potential areas of interest.

Final interpretation, decision-making, and accountability must remain human. Pharmacovigilance is not only about data analysis. It is about ethical responsibility to patients and society.

AI will continue to evolve. Until it can truly understand context, uncertainty, and responsibility, its role in pharmacovigilance should remain supportive rather than authoritative.