Insight on AI – April 2026, Issue 9
Welcome to the April, Issue 9 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.
Tech News
AI in Clinical Trials is Moving Fast — But Trust Will Decide the Pace
AI is transforming clinical trials by accelerating patient selection, improving safety monitoring and uncovering patterns across vast datasets. From electronic health records and imaging to genomics and historical trial data, AI can process information at a scale and speed that was previously impossible.
The potential is significant. AI can streamline site selection, identify eligible participants faster and even support the design of new drug candidates. In theory, this should shorten development timelines, reduce costs and improve trial outcomes.
Yet despite this promise, widespread adoption still depends on trust. AI systems are only as reliable as the data they are trained on — and clinical data is often incomplete, fragmented or biased. If underrepresented populations or rare events are missing from the data, AI outputs may reinforce those gaps rather than correct them.
Transparency is another challenge. Many advanced models operate as “black boxes,” making it difficult for regulators, clinicians and researchers to understand how conclusions are reached. In a highly regulated environment, explainability is essential.
This is why human oversight remains critical. AI can surface insights and reduce operational burden, but it cannot replace clinical judgement or contextual understanding. The future of AI in trials will depend not just on technological progress, but on building confidence through validation, transparency and responsible use.
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AI News
AI’s Growth Has a Physical Limit — and Infrastructure is the Real Challenge
AI may feel limitless in capability, but behind every model lies a hard physical reality: infrastructure. Data centres, servers, chips and cooling systems are the foundation of AI — and they require vast amounts of electricity, water and long-term planning.
Today, data centres already account for around 2% of global electricity use, and AI demand is pushing that figure higher. Projections suggest energy consumption could double by 2030, reaching levels comparable to entire nations.
The environmental impact is becoming harder to ignore. AI infrastructure consumes enormous volumes of water for cooling, contributes to carbon emissions and, in some cases, is even affecting local temperatures. Much of today’s AI progress has been driven by brute-force scaling: more computing power, more chips and more energy.
This raises important questions about sustainability. AI is no longer just a software issue — it is increasingly a power grid issue. Scaling AI responsibly will require investment in renewable energy, smarter cooling systems and more efficient hardware.
There is reason for optimism. New advances in chip design, cooling technologies and energy-efficient models are beginning to reduce AI’s footprint. AI may also help optimise energy systems in other sectors.
The challenge ahead is not whether AI can continue to scale, but whether it can do so sustainably. Constraints may ultimately become the driver of the next wave of innovation.
Industry
Why Human Oversight Matters More Than Ever in Pharmacovigilance
AI and automation are reshaping pharmacovigilance, making safety reporting faster, signal detection earlier and workflows more efficient. Tasks that once took weeks can now be completed in minutes.
But beneath these gains lies a quieter, more important shift: the role of the pharmacovigilance professional is changing. As automation takes over routine processing, human expertise is becoming more valuable — not less.
AI is highly effective at detecting patterns, but it struggles with nuance. A spike in reports may reflect greater awareness rather than a true safety issue. Rare but serious adverse events may remain overlooked because they lack statistical weight. Data alone cannot provide the full picture.
Bias is another concern. Pharmacovigilance data has long been uneven across geographies, healthcare systems and patient populations. AI systems trained on incomplete or skewed data may amplify these blind spots if left unchecked.
This is why human oversight remains essential. Pharmacovigilance professionals are increasingly responsible for interpreting AI outputs, validating findings and ensuring decisions are transparent and defensible.
As automation becomes more embedded in everyday workflows, the future of pharmacovigilance will depend on how effectively people and AI work together. In a field where decisions directly affect patient safety, human judgement remains the critical safeguard.
Regulation
UK Regulators Are Signalling the Beginning of the End for Animal Testing
The UK’s MHRA has introduced new guidance that could significantly reduce — and eventually replace — the role of animal testing in drug development. The shift reflects growing confidence in AI-driven, human-relevant methods for predicting drug safety and efficacy.
At the centre of this change are New Approach Methods (NAMs), which include AI-powered analysis, human-derived cell models and other advanced computational tools. These technologies can provide more relevant insights into human biology than traditional animal studies.
The regulatory implications are significant. Under the new guidance, animal testing will face stricter limitations, with certain drugs no longer eligible for animal studies and toxicity testing increasingly restricted to cases where it is clearly justified.
Importantly, the MHRA is also creating pathways for developers to submit early non-animal evidence. This opens the door to a more collaborative and iterative review process, moving away from rigid, linear evidence requirements.
For pharmacovigilance and drug safety teams, the benefits could be substantial. Earlier and more accurate toxicity prediction may reduce costly late-stage failures and improve the quality of post-market safety monitoring.
While this does not mean the end of animal testing overnight, it marks a clear shift in direction. The future of drug development is likely to rely more on human-relevant evidence — with AI playing a central role in making that possible.
