AI in Pharmacovigilance Case Processing: What It Does Well, and What It Will Never Touch

By Dorothy Ogwuru, Founder, PharmaLink Academy. 18 years in pharmacovigilance, regulatory medical writing and pharmaceutical industry capability development. Last updated 5 October 2026.

When I started in pharmacovigilance, we processed cases on paper. E2B submissions were done by hand, and a single case could take a meaningful part of your day. Over the years I watched that change. Paper became manual electronic entry. Manual entry became electronic E2B, where a submission was, in effect, the click of a button. The administrative weight of case processing has been falling away for two decades. AI is not a break from that trend. It is the next step in a line that started long before anyone was talking about machine learning.

So when people ask me whether AI is going to take over pharmacovigilance, I do not react the way they expect. The structured parts of case processing were always going to be supported by a machine at some point, with the right oversight. That is not a threat to the profession. It is a shift in where your time goes. What follows is an honest account of what AI genuinely does well in PV in 2026, where it quietly fails, and the part of the work that no machine is going to touch, because the regulator will not permit it to.

What case processing actually is, and why a machine was always going to help with it

Case processing is where most pharmacovigilance professionals learn the discipline, and that grounding matters. It is also, by its nature, structured work. A large part of it is validation. Is this a valid case. Does it have the four minimum criteria. Is it serious or not. What is the causality. These are decisions made against a reference document, and decisions made against a reference document are the kind of task that a machine can support well. That has been true for a long time, which is why the admin around case processing has been getting lighter for two decades, well before AI entered the conversation.

As I moved into more senior roles, my work shifted from the volume of case processing toward the judgement that sits above it. That progression is the natural shape of a pharmacovigilance career. It is worth understanding before you decide what AI does and does not change, because the foundational years of case work are not being erased. They are being made lighter, so that the people doing them reach the more demanding, more interesting judgement work sooner.

What AI does genuinely well in 2026

I think AI does a strong job on case processing, and I do not say that grudgingly. The reason it works is that you are feeding the system the Summary of Product Characteristics. Provided it holds the most up to date version of the SmPC, it can move quickly through the structured questions. It can determine whether a case is valid. It can apply the seriousness criteria. It can reach a causality assessment, because the reference point it needs is already in front of it.

The other thing it does well is narrative writing. This used to be a real part of the daily PV workload. AI takes a body of information, orders the events by date, and uses that sequence to tell the story of the case. It drafts a narrative very competently. That is genuinely why narrative writing is no longer the heavy manual task it once was for a PV associate. The first draft can be automated, and the human role becomes finessing it rather than building it from nothing.

Where AI quietly fails, and why a human still signs off every case

Here is the part that matters, and the reason we cannot lean on the machine. AI will miss things, even with the SmPC in front of it. There are terminologies it does not catch. There are events it will not classify as related to the medicinal product when, to an experienced reviewer, the relatedness is there. Confounding factors are an area where I do not believe it performs well at all. These are not rare edge cases you can wave away. They are the difference between a case handled correctly and a case where something has slipped through the net.

This is precisely why a pharmacovigilance person is still required to review these cases. Someone has to confirm the seriousness criteria were applied correctly. Someone has to check the causality call. Someone has to look for the confounding factors the machine did not weigh. The automation drafts and proposes. The human verifies and decides. Take the human out of that loop and you do not get efficiency, you get errors that nobody caught until a patient was affected.

The part of pharmacovigilance no machine will touch

The side of PV I know cannot be taken over is the heavy clinical judgement, and the clearest example of it is the safety review committee. Different companies call it different things, a safety review committee, a CRC, an SRC, but the work is the same. A signal has surfaced in the global safety database. The pharmacovigilance professionals now have to investigate what is actually causing it.

That investigation is not a structured form. It is a full deep dive. You review the cluster of cases from the global safety database, individually, to determine causality and confounding factors and whether the events are genuinely related to the product. You look at the class effects of that drug and of other drugs on the market, to see whether what you are observing is a class effect rather than something specific to your product. You review the literature. Then you reach an overall conclusion across the whole body of data, and you decide whether it needs to be communicated to protect patients.

AI cannot do that. It needs your clinical judgement. It needs your investigative instinct. Spotting a genuine trend across a cluster of cases, recognising the nuance, connecting the dots between database signals and class effects and the literature, that is human work. A machine will struggle to see the pattern, and where it does miss, the consequences land on real patients. This is the same investigative discipline that sits behind a well-run global pharmacovigilance system, and it is the work the senior end of the profession is built around.

Why the regulator will not allow a machine to be accountable

There are two reasons AI will not replace pharmacovigilance, and they sit above any question of capability.

The first is structural. Pharmacovigilance is a mandatory function. Under the regulations, every company holding a marketing authorisation must have a PV department. A company cannot simply decide it would rather not. That requirement does not soften because a tool got better at drafting narratives.

The second is accountability, and it is the one that settles the argument. Regulators will not accept a machine as the responsible party. If a safety issue surfaces tomorrow, no sponsor can turn to the regulator and say the AI must have got it wrong. The moment you try that, the question that comes back is simple. You had a machine overseeing this. Who was overseeing the machine. The accountability falls straight back onto the pharmaceutical company, onto the sponsor, where it always sat. Regulators do not fine an algorithm. They penalise the company, and no sponsor wants that penalty or the reputational damage that comes with it. So AI cannot be your excuse, and a human must always be the one providing oversight, because what is at stake is patient safety and patients' lives, and everyone in the room knows it.

If you are hesitating to train in PV because of AI

If someone came to me unsure about training to enter pharmacovigilance because they feared AI would make the role redundant, my answer would be direct. Do not hesitate. If anything, this is the right time to move into the industry, precisely because you will not be carrying the heavy admin that defined the entry-level role a decade ago.

I found that admin genuinely interesting at the time. But as you grow, what you actually want is to be involved in the decisions, the important calls about the safety profile of a drug and how that gets communicated. That is the exciting part of the work, and it is the part AI will not be taking over, because it needs people with real clinical judgement who can weigh a drug's safety profile against the reality that patients' lives depend on the call.

The sector is not shedding people. PV remains a high value role, and I am not seeing redundancies in it driven by automation. What has changed is the shape of the associate role itself. The admin has reduced sharply, and in its place companies are pushing more local judgement and oversight into the role, along with exposure to signal detection and clusters earlier than before. In other words, the junior role now starts closer to the interesting work than it used to. That is a better entry point, not a worse one. If you are weighing how to train for it, the companion pieces on what a pharmacovigilance certification actually proves, on live PV training versus pre-recorded courses and on getting safety database experience before your first role are the right next reads, because the way you train now matters more than it did when the job was mostly admin.