As the landscape of drug safety changes, so does the pharmacovigilance market. While PV has historically been conservative due to heavy regulation, the sheer volume of safety data is driving a shift toward AI.
It brings the question: do we need AI automation to the extent we think we do? Is the adoption of AI actually going to replace scientific judgment with algorithmic probability?
Or will AI free us to focus more on the complex, high-value safety decisions that actually require expertise? The real question isn’t whether we are ready for AI, it’s whether we are willing to redefine our role in relation to it.
Most experts agree that while AI will accelerate pharmacovigilance, it cannot replace human judgment. AI requires strong governance and regulatory alignment; without proper oversight, automated systems risk creating more errors than they prevent.
The real question isn’t whether humans are necessary, but why human expertise remains indispensable and how much human-in-the-loop oversight is required at each stage.
What is human-in-the-loop AI?
Human-in-the-loop (HITL) is an approach in which people actively participate in, supervise, or validate an AI system’s decisions. This is especially important when accuracy, safety, or compliance matters. It becomes all the more significant in a regulatory environment.
Instead of being fully automated, HITL incorporates human judgment into labeling data, reviewing edge cases, and approving high-risk outputs. For instance, in clinical development, we can see how humans work collaboratively with AI in production systems.
When it comes to pharmacovigilance, an alternative, i.e., complete automation, can disrupt the game altogether. The question becomes: when AI can perform a task, should it rely entirely on itself, or should a human be involved at some point in the process?
Before we delve into it, let’s go through the other buzzwords that have evolved alongside HITL:
What is Human-on-the-Loop (HOTL)?
This approach is one in which the AI operates autonomously, but a human oversees the process. The human has veto power to hit the kill switch or override the system if things go haywire.
What is Human-out-of-the-loop (HOOTL)?
This gives AI full autonomy, with no human interruption whatsoever. Here, AI senses, decides, and acts without any human intervention.
How is human-AI collaboration evolving in pharmacovigilance?
When we are talking about pharmacovigilance or the pharma industry in general, it’s all about patients. HITL represents an ecosystem where humans and AI work in tandem.
In 2025, more than 1.7 million adverse drug reaction (ADR) reports were submitted to EudraVigilance, similar to the number in 2024. About 64% of reports originated from outside the European Economic Area (EEA).
With this cumbersome volume of reports, one can only wonder about the time required for review and signal detection. AI has been a herculean helper in this scenario.
Here is how HITL has benefited the pharmacovigilance industry:
Higher accuracy
HITL implementations across healthcare consistently improve decision accuracy compared with AI or humans working alone. Some production HITL workflows achieve up to 99.9% accuracy.
Greater reviewer agreement
A 2026 HITL implementation for evidence review achieved 95% agreement between AI and expert reviewers (range: 89–99%), with only 5% of records requiring human arbitration.
Higher productivity
HITL workflows have demonstrated up to 5× faster processing while maintaining quality.
Reduced manual workload
In literature-review workflows, AI produced 90% submission-ready outputs, while experts focused on validation and regulatory judgment.
Faster validation cycles
Pharmaceutical HITL implementations shortened validation activities by 25–40 % while improving reviewer consistency.
Improved regulatory compliance
The EU AI Act explicitly requires effective human oversight for high-risk AI systems, making HITL a key governance mechanism.
How does AI make nuanced judgments in pharmacovigilance?
AI has revolutionized pharmacovigilance, but it does not replace the need for human judgment and accountability.
In the context of aggregate safety reporting, such as PBRERs and DSURs, AI improves literature screening speed, case prioritization support, structured formatting, and early signal detection.
However, regulatory acceptance is not driven by speed; it is driven by defensible interpretation.
The AI models, although sophisticated in Natural Language Processing (NLP), face insurmountable challenges in the PV landscape:
Case prioritisation complexity
AI systems often seem to misinterpret borderline cases. Such cases could be reports with vague symptom descriptions, leading to false negatives that miss genuine safety signals or false positives that overwhelm review queues.
Temporal relationships
AI assesses whether the timing of an adverse event is consistent with the suspected medicinal product. For example, did the event occur after treatment started? Did symptoms improve after the drug was discontinued? Did they recur after the drug was restarted? Timing alone does not establish causality, but it is an important part of the overall assessment.
Also, an AI algorithm may suggest heightened reporting of dizziness with a medication, but the latest article might indicate that this reflects increased awareness rather than an actual risk.
Common Risk Factors & Error Modes in Fully Automated Systems
The term ‘full automation’ refers to the errors, limitations, and risks that AI systems may encounter when automating pharmacovigilance tasks such as case intake, literature screening, coding, signal detection, and regulatory reporting.
Here are a few examples:
| Error Type | Description | Example in Pharmacovigilance |
| False Positives | AI incorrectly identifies an event or signal. | Flags a non-case as a valid ICSR. |
| False Negatives | AI misses a genuine event or signal. | Fails to detect a serious adverse event in a case narrative. |
| Misclassification | AI assigns the wrong category or label. | Classifies a serious case as non-serious or selects an incorrect MedDRA term. |
| Information Extraction Errors | AI extracts incorrect or incomplete data. | Misses the onset date or confuses the suspect and concomitant drugs. |
| Hallucinations | AI generates information that is not present in the source. | Invents symptoms or patient details not mentioned in the report. |
Why is full automation not best fit in pharmacovigilance?
The cumbersome part of AI in pharmacovigilance was never AI itself! It’s the lack of interaction between humans and AI.
Our team has incorporated AI in the process and achieved significant results. Case intake and literature assessments are an integral part of it.
Like everyone, we were also excited about AI in drug safety. Faster cases with less human interaction.
Even though AI can automate case intake, enable MedDRA coding, and detect signals earlier, it still misses out on some things.
- It understands patterns, but not meanings, which implies subtle nuances and new patterns can still be overlooked.
- In case your source narratives are weak, it amplifies the problem even more.
- Regulators emphasize the decision of humans, not machines.
This is why the future of PV isn’t entirely AI automation.
It’s AI + human oversight.
We aren’t looking into how much we can automate. Rather, our focus is on where AI actually augments human decision-making.
If you believe that human-in-the-loop is the future, we can be a trusted partner for you.
Book a FREE consultation with us now, and let’s work together to make your pharmacovigilance project a success.
FAQs
- What is Human-in-the-Loop (HITL) in pharmacovigilance?
Human-in-the-Loop is an AI governance approach where pharmacovigilance professionals review, validate, and refine AI-generated outputs. Instead of replacing human expertise, AI assists with repetitive tasks while qualified safety experts make the final clinical and regulatory decisions.
- Why is HITL important in AI-driven pharmacovigilance?
Pharmacovigilance involves complex clinical judgments that require contextual interpretation. HITL helps ensure AI outputs are accurate, explainable, and compliant with regulatory requirements while reducing the risk of false positives, false negatives, and inappropriate classifications.
- Which pharmacovigilance processes benefit the most from HITL?
HITL is commonly used in:
- ICSR case intake and validation
- Case triage
- MedDRA coding
- Literature screening
- Signal detection
- Duplicate detection
- Quality control (QC)
- Benefit-risk assessment support
- Aggregate report preparation (PBRERs, DSURs)
- Can AI replace pharmacovigilance professionals?
No. AI is designed to augment, not replace, pharmacovigilance experts. While AI can automate repetitive tasks and analyse large datasets, medical judgment, causality assessment, signal validation, and regulatory decision-making remain the responsibility of qualified professionals.
- How does HITL improve patient safety?
Human oversight helps identify AI errors before regulatory submission or clinical decision-making. Expert review reduces the likelihood of missed serious adverse events, incorrect classifications, and incomplete safety assessments, ultimately supporting better patient protection.
- How does HITL reduce AI errors?
Human reviewers verify AI outputs, resolve ambiguous cases, correct misclassifications, and validate extracted information. Their feedback can also be used to retrain AI models, improving accuracy and performance over time.