Regulatory expectations in the drug safety industry have evolved faster in the past five years than in the previous two decades combined. The US Food and Drug Administration (FDA), the European Medicines Agency (EMA) and other global authorities now expect safety reporting to deliver comprehensive, reliable and transparent data. Recent FDA rejections demonstrate how high these standards have become. Even strong therapies can be delayed or denied when gaps in trial design, inconsistencies in safety submissions or unaddressed risks prevent regulators from seeing the full picture.
Right now, a broader shift is reshaping pharmacovigilance (PV). Traditionally, the focus was on producing high volumes of safety information. Today, regulators expect data that is not only accurate but contextual and defensible, as well. With patient interactions expanding across digital channels and safety data growing more complex, life sciences organisations are leveraging artificial intelligence (AI) and intelligent automation to ensure that every medically relevant detail is captured, connected and ready for review.
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These shifts are driving new approaches to drug safety submissions and advancing models grounded in AI-ready PV. The next generation of PV prioritises data quality, transparency and real-time insight into how therapies perform in the real world.
From fragmented signals to connected safety intelligence
Earlier safety data environments were slower and more linear, relying on keyword searches, predefined rules and manual review to detect adverse events. While this approach was sustainable when data came through a handful of controlled channels, it is no longer sufficient. Today, safety-related data streams are surging from call centres, support programmes, clinical notes, emails, videos, social media posts and patient-generated content. Each source can contain a wealth of clinically relevant information, yet much of it is unstructured and difficult to analyse without modern tools.
Regulators now expect PV teams to capture more than obvious side effects. Submissions must also account for potential medication errors, lack of efficacy, off-label use, unexpected benefits, pregnancy exposure and device malfunctions. Patients may describe symptoms informally, while clinicians document events in nuanced language. Without advanced analysis, this discrepancy allows critical insights to go undetected.
Artificial intelligence (AI) enables teams to extract and convert unstructured, complex information into structured, analysable records. Once structured, the data can reveal subtle clues, such as changes in patient mobility, inconsistent dosing or emerging symptoms. This two-step approach can also be replicated at scale to find patterns across vast datasets and give safety teams a more complete and transparent view of what is happening across patient populations.
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By GlobalDataEvolving standards demand updated regulatory guidance
In early 2025, the FDA published its draft guidance for industry and other interested parties, “Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products.” In it, the FDA directs drug and biologics sponsors to evaluate and document the credibility of any AI model used to support regulatory decisions, focusing on data quality, methodological transparency and fit-for-purpose validation.
The FDA makes it clear that sponsors must ensure AI-generated evidence is traceable, explainable and backed by a risk-based assessment. The agency provides a seven-step credibility framework that asks sponsors to:
- Define the question of interest: First, clearly define the decisions or the problem that an AI model will address.
- Specify the context of use: Elaborate on the role and scope of the AI model as well as how PV teams will utilise its outputs.
- Assess model risk: AI is not infallible. This step is to determine to what extent AI may influence decisions, and the severity of the consequences should the model be incorrect.
- Build a credibility plan: The organisation must establish and evaluate the model’s credibility for its intended use.
- Execute the credibility plan: The only way to know whether a credibility plan is comprehensive is to fully test it.
- Document results: Once the organisation tests the plan, it should summarize what the assessment found and document any changes from the original plan.
- Determine adequacy of the model: The final step is to determine whether the AI model provides sufficient credibility for its intended regulatory purpose.
Regulators are not looking to drown in waves of data. Instead, they want data that is better explained.
How PV teams can meet regulatory requirements
As the conversation around safety shifts from the overall volume of data being generated and submitted to a focus on the accuracy and transparency of every element submitted for review, organisations must adapt their processes accordingly.
To meet regulators’ evolving expectations, PV teams are integrating AI into their workflows to enhance their already rigorous safety reporting.
Key benefits include capturing subtle or complex signals, such as device malfunctions, medication errors or lack of efficacy that would otherwise be missed, consolidating fragmented data streams from transcripts, support programmes, social platforms and call centres into a unified safety record, and delivering a clearer picture of real-world performance to regulators, which reduces the likelihood of rework or delays.
Right now, organisations are elevating PV teams from a compliance function to a strategic enabler of regulatory and patient outcomes.
Intelligent automation and the closing of reporting gaps
As the volume and complexity of data continues to grow, life sciences organisations are increasingly turning to intelligent automation to serve as a management tool. These automation tools can help teams develop cases for regulatory submission and identify contextual nuances that human reviewers may overlook. This combination of speed and precision allows safety teams to reduce inconsistencies between data sources, accelerate case processing and narrative generation, improve the accuracy of submissions, and focus skilled staff on analysis instead of administrative tasks.
Automation is not meant to replace expert judgment. Instead, intelligent automation should be leveraged to augment the human-in-the-loop approach by removing the burden of manual reviews and freeing teams to apply their expertise where it is most valuable.
Orchestrating the safety lifecycle through agentic AI
Technology is advancing quickly, and the next frontier of AI is already taking shape. Agentic AI operates beyond automation and into true orchestration, offering systems that can monitor safety workflows in real time and initiate proper actions without constant human oversight. In PV, developers are designing these agentic systems to support faster detection, smarter decision-making and more resilient safety operations.
Agentic AI can monitor call transcripts, email and safety databases as they are updated. With this continuous flow of information, digital agents can flag potential issues, route high-risk cases to reviewers and ensure deadlines are met so that no urgent signal is overlooked. According to McKinsey, agentic capabilities can alleviate 25 to 40% of the time teams spend on repetitive, manual or administrative tasks. With this valuable time being reallocated, PV teams can focus on tasks that require their expertise and provide greater value.
This evolution moves PV toward a more proactive model. AI-driven workflows can help teams identify risks earlier and ensure that the right experts receive the right information without delay.
Building trust through transparency and governance
Implementing AI is not as simple as flipping a switch. Successful programmes rely on unified data architectures that integrate call center systems, customer relationship management tools and document repositories. These components allow organisations to meet rising regulatory requirements while maintaining transparency.
At the center of this technological evolution is a simple truth: strong governance is essential. Teams must validate all AI-generated outputs with the same rigour applied to manual work. This includes comprehensive quality checks, audit trails and human-led review, all to ensure that safety decisions remain grounded in expertise.
This pressure on validation ensures greater trust. Regulators are looking at how models operate, how data flows through the system and how final decisions are made. With greater transparency comes regulatory confidence and acceptance.
A more proactive future for drug safety
Driven by rising regulatory expectations and rapid technological advancement, PV is shifting from retrospective reporting to a more proactive, intelligence-driven discipline. As organisations expand their use of AI and automation, these tools are delivering value far beyond operational efficiency. They are enhancing scientific integrity and strengthening risk mitigation.
The future of PV will be defined by intelligent collaboration. Human expertise combined with the processing power of AI systems will deliver faster and more accurate insights. This partnership will help close gaps in safety, reinforce monitoring throughout the entire lifecycle of safety review and support better outcomes for patients and regulators.
