Insight on AI – October 2026, Issue 19
Welcome to the October, Issue 19 edition of Artificial Vigilance, Essjay Solutions, Insight on AI, dedicated to helping pharmacovigilance professionals understand, engage with and adapt to the rapidly evolving world of artificial intelligence.
Regulation
What can UK AI Healthcare Regulation Learn from The Pharmacovigilance Approach?
Pharmacovigilance has long operated on the principle that regulatory approval is not the end of safety assessment, but the beginning of continuous monitoring. Now, the UK Government is moving towards applying a similar philosophy to AI in healthcare.
The UK Government has announced it will accept all 44 recommendations made by the National Commission into the Regulation of AI in Healthcare, signalling a shift in how AI may be regulated across the NHS. Their response outlines ambitions to replace traditional one-off assessments with a flexible framework that continuously monitors AI systems throughout their lifecycle.
The independent NHS doctor-led commission developed its recommendations through consultation with over 12,000 patients, clinicians and members of the public. It argues that existing regulatory approaches must evolve to address fluctuating AI performance after deployment, making static assessments inadequate.
The proposed lifecycle-based framework would emphasise evaluating AI systems in real-world clinical environments, rather than relying predominantly on pre-deployment assessments. The MHRA is already exploring this approach through its AI Airlock regulatory sandbox, focusing on post-market surveillance of AI-enabled medical devices.
Innovation
Could AI Predictions Make Polypharmacy Pharmacovigilance Prophylactic?
What if pharmacovigilance could identify dangerous drug interactions before patients ever experience them? Recent breakthroughs suggest AI could predict and potentially prevent adverse reactions from drug combinations.
DCSE (Drug Combinations Side Effects) is a machine learning model developed by researchers Ruben Jimenez and Alberto Paccanaro to predict adverse reactions from combinations of different medicines. They seek to address a longstanding challenge in drug safety: clinical trials realistically cannot investigate every possible drug combination, so many potentially harmful interactions only emerge when these medicines are combined in larger populations.
DCSE works by learning numerical representations, called latent signatures, of individual medicines, drug combinations and adverse events. By identifying known patterns within existing drug safety data, the model can estimate the likelihood of specific side effects occurring when two medicines are taken together. DCSE can even make predictions for drug combinations with no previously documented adverse reactions.
Although traditional pharmacovigilance has focused on identifying safety signals from adverse reactions that have already happened, predictive models like DCSE could invite an additional preventative layer, allowing safety teams to investigate potentially dangerous combinations before patient exposure.
Industry News
Right Patient, Right Trial: How AI Can Transform Clinical Trial Recruitment
Finding the best patients for clinical trials is one of the pharmaceutical industry’s most persistent challenges. Recruitment can take months or even years, delaying research and limiting patients’ access to investigational treatments. However, AI may hold the answer to make the process faster, more efficient and potentially more inclusive.
Pfizer is experimenting with AI-assisted recruitment to identify eligible patients, optimise the locations of the trials and streamline the design of studies. Rather than relying on manual review of medical records, AI can analyse clinical information against complex eligibility criteria and identify candidates who might otherwise have been overlooked.
The potential benefits are supported by independent research. Researchers from the US National Institutes of Health developed TrialGPT in 2024, an AI system that matches patient information with clinical trial criteria. TrialGPT achieved 87.3% accuracy when assessing individual eligibility criteria and in a small pilot, clinicians reported a reduction in screening time of 42.6% without a significant difference in accuracy.
For pharmacovigilance professionals, better representation during development could mean better safety knowledge when medicines reach the wider population, yielding drug safety benefits beyond simple efficiency and automation.
AI News
When the AI Bubble Bursts, What Happens to Pharmacovigilance?
The AI boom has attracted trillions in investments, but growing concerns about debt, profitability and unsustainable spending are raising an uncomfortable question: what happens if the AI bubble bursts? For PV professionals increasingly reliant on AI-powered systems, the consequences could extend beyond financial markets.
Technology giant Oracle serves as a concerning example. According to Reuters, the company had accumulated $129.5 billion in debt by June 2026, alongside $260 billion in long-term data centre lease commitments. Its aggressive leveraged investment strategy has led to its credit being downgraded to BBB–, just one level above junk status.
Oracle is also invested in drug safety. Its Argus safety platform supports adverse event processing, safety data management and regulatory reporting. They recently announced a partnership with Wipro to strengthen AI-powered PV services using its cloud-based Argus business services model.
So what happens if the bubble bursts? While a collapse in AI valuations would not instantly disrupt PV, a prolonged investment downturn could have more serious consequences. Reduced funding could force smaller AI vendors to scale back development, increase prices or cease operations. PV companies relying on these vendors may have their automation projects disrupted, lack technical support or face unexpected costs to replace systems.
AI isn’t going anywhere; the dotcom bubble didn’t delete the internet. But perhaps it is now worth considering not just the practical benefits of an AI system, but the financial sustainability of its provider.
