Artificial intelligence (AI) is rapidly transforming the way safety monitoring is conducted in healthcare. Nowhere is this transformation more visible than in pharmacovigilance (PV), where the traditional model rooted in spontaneous reporting systems and manual case processing is being reshaped by automation, advanced analytics, and real-world data (RWD).
But this evolution goes beyond drugs. It is giving rise to a broader paradigm: multivigilance. This approach integrates multiple vigilance disciplines, recognizing that today’s health products are more interconnected, more complex, and more reliant on data than ever before. AI and RWD are the driving forces behind this shift, enabling a more holistic and proactive approach to safety.
AI in Pharmacovigilance: From Automation to Augmentation
Over the past decade, the volume and complexity of safety data have increased significantly. Global pharmaceutical companies now handle millions of Individual Case Safety Reports (ICSRs) annually, much of which requires careful review, coding, and regulatory submission. AI is being adopted to handle this burden more efficiently and consistently.
Key applications of AI in PV include:
- Natural Language Processing (NLP) to extract adverse events from medical literature, call center transcripts, and social media.
- Machine learning (ML) models to prioritize or triage cases based on risk or regulatory urgency.
- Automated narrative generation to standardize safety case reports.
- Signal detection algorithms to identify emerging safety concerns from structured and unstructured data.
Importantly, regulators like the European Medicine Agency (EMA) and the Food and Drug Administration (FDA) are increasingly open to these technologies, provided they are implemented with transparency, validation, auditability, and human oversight. AI is not a replacement for safety experts but a tool to augment their capabilities and ensure quality and consistency at scale.
The Role of Real-World Data in the AI-Powered Vigilance Ecosystem
AI may be the engine, but RWD is the fuel. Traditionally, PV relied on spontaneous reports submitted by healthcare professionals, patients, and manufacturers. While valuable, these reports are often underreported, incomplete, and lack context.
Today, RWD sources provide a much richer picture of how therapies are used and what risks may emerge in everyday settings. These sources include:
- Electronic Health Records (EHRs)
- Administrative claims databases
- Registries (disease-specific, treatment-specific)
- Wearables and connected devices
- Mobile health apps and patient-reported outcomes
- Social media and online forums
These data enable a range of enhanced PV activities:
- Signal validation and quantification: Assessing how common a reported adverse event really is.
- Understanding at-risk populations: Evaluating comorbidities, polypharmacy, and sociodemographics.
- Contextualizing drug use: Investigating off-label use, adherence, and switching patterns.
- Comparative safety: Benchmarking across therapeutic classes or similar products.
However, data quality, standardization, access, and regulatory acceptance remain challenges. Not all RWD is fit-for-purpose, and each source has limitations that must be understood. Therefore, triangulation of multiple data sources and methods is often needed to create a reliable safety profile.
From Pharmacovigilance to Multivigilance
With increasingly complex products and interdependent therapies, safety monitoring is expanding beyond drugs to encompass devices, diagnostics, biologics, and more. This shift has led to the emergence of multivigilance, a concept recognizing the need for integrated vigilance across multiple product domains.
| Vigilance Type | Focus Area |
| Pharmacovigilance | Drugs and biologics |
| Materiovigilance | Medical devices and diagnostics |
| Vaccinovigilance | Vaccines and immunization safety |
| Biovigilance | Blood, tissues, and cellular therapies |
| Nutrivigilance | Nutritional supplements and fortified foods |
| Cosmetovigilance | Cosmetics and personal care products |
| Herbovigilance | Herbal and traditional medicines |
The need for multivigilance is growing due to:
- Combination products (e.g., drug-device systems, digital therapeutics)
- Shared pathways of harm (e.g., hypersensitivity, device malfunctions)
- Public health events requiring coordinated monitoring across product types (e.g., pandemics, contaminated batches)
AI plays a vital role in making multivigilance feasible. Its ability to process vast and heterogeneous datasets allows safety professionals to identify cross-product signals and uncover novel safety concerns. Meanwhile, RWD provides the context and scale necessary to validate and act on these findings.
Challenges in Building a Multivigilance Framework
Despite its potential, multivigilance faces real barriers:
- Fragmented data ecosystems: Disparate systems across countries and product classes make integration difficult.
- Varying regulatory frameworks: Each product type and geography may follow different safety reporting rules.
- Limited AI readiness: Many organizations lack the clean, labeled datasets required to train effective models.
- Privacy, consent, and governance: Sharing patient-level data, even pseudonymized, is tightly regulated.
Additionally, interpreting real-world evidence (RWE) from diverse data sources requires specialized expertise, including epidemiology, clinical informatics, and data science, to avoid biased or misleading conclusions.
Toward a Smarter, Safer Future
The convergence of AI, RWD, and multivigilance thinking marks a turning point in how we ensure product safety in the real world. It enables us to move beyond reactive reporting to a more proactive, intelligent, and patient-centric model.
Multivigilance is not about adding complexity. It is about aligning systems, data, and insights across the health product lifecycle to detect and mitigate risk more effectively. It is about using RWD to bridge the evidence gap between trials and practice. And it is about using AI to identify patterns humans alone might miss.
In a world of hybrid therapeutics and real-time health monitoring, we need safety systems that are as integrated and adaptive as the products they monitor. Multivigilance offers that opportunity and with the right governance, investment, and vision, it can become the foundation of a safer, smarter healthcare system.
