Insight on AI – March 2026, Issue 6
Welcome to the March, Issue 6 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
Size Doesn’t Matter; It’s How You Use It: The Rise of Small Language Models in PV
Bigger is not always better. Small language models (SLMs) are emerging as lean, task-specific alternatives to large language models (LLMs) in regulated environments like pharmacovigilance.
Unlike generalist LLMs trained on broad internet data, SLMs are trained on carefully curated domain-specific corpora, such as medical and adverse event reporting data. The result is greater precision, faster response times, improved determinism and easier auditability.
SLMs can operate within secure local environments, reducing privacy risk while increasing governance control. Early adopters report measurable gains — including one Swiss pharmaceutical company that replaced an inconsistent LLM-based PV tool with a custom SLM tuned to its own SOPs, reducing output variance by 72% and accelerating compliance approvals.
Industry forecasts predict that by 2027, organisations may deploy three times as many specialised SLMs as general-purpose LLMs, reflecting a broader shift toward accountable, in-house AI infrastructure.
For PV teams facing budget constraints and regulatory scrutiny, smaller, more controlled models may represent a safer and more sustainable path to AI implementation.
AI News
Is Privacy Overrated? Should We Sacrifice Patient Confidentiality for Better Data?
AI is transformative — but it is also data hungry. To enable faster signal detection and earlier adverse event identification, AI systems require vast datasets. This raises a critical question: should privacy constraints be relaxed to improve data access?
Broader, cross-border datasets increase the likelihood of identifying rare adverse reactions earlier and improve representativeness across diverse populations. In public health emergencies, rapid data pooling could accelerate benefit–risk assessments and potentially save lives.
However, pharmacovigilance depends on trust. If patients and healthcare professionals fear inadequate data protection, reporting behaviour may decline. Underreporting and incomplete details degrade data quality, ultimately weakening AI performance.
Legal and ethical frameworks such as GDPR exist to protect personal autonomy and dignity. Weakening privacy safeguards increases legal, reputational and cybersecurity risks — particularly when large centralised datasets become targets for breaches.
Ultimately, AI performance depends more on governance and data quality than on sheer volume. Safety does not compete with privacy; it depends on it. Sustainable innovation requires secure systems, advanced anonymisation techniques and transparent oversight.
Trust is not a barrier to innovation — it is its foundation.
Industry
Takeda’s $1.7bn Deal for AI Drug Discovery
Takeda Pharmaceutical has signed a landmark multi-year agreement with Iambic Therapeutics valued at up to $1.7 billion — one of the largest AI-focused drug discovery deals to date.
The partnership gives Takeda access to Iambic’s NeuralPLexer platform, a physics-trained AI model designed to predict how small molecules bind to proteins. By integrating AI predictions into a rapid “design, make, test, analyse” loop, Takeda aims to compress discovery timelines dramatically — potentially reducing programmes that previously took up to six years to under two in certain cases.
However, speed is not the sole objective. Takeda’s leadership emphasises improving candidate quality and decision confidence to reduce downstream risk and resource waste.
For PV and discovery teams, this deal signals a broader shift: AI is transitioning from experimental pilot projects to core R&D infrastructure. Its influence will extend beyond discovery into clinical candidate selection and ultimately into safety evaluation workflows.
The ripple effects for pharmacovigilance may be substantial.
Innovation
Quantum Computing: The Final Frontier of Pharmacovigilance Intelligence
Quantum computing, poised to dwarf the power of classical systems, promises to tackle complex pharmacovigilance challenges that would take even the most advanced supercomputers centuries to solve.
Unlike classical computers that operate in binary (0 or 1), quantum systems use qubits that represent multiple states simultaneously. This enables the rapid analysis of vast, multi-dimensional safety datasets to detect subtle adverse event patterns faster and more accurately.
Quantum machine learning could significantly enhance signal detection by evaluating countless combinations of risk factors, concomitant therapies and genetic variables in parallel. Its most transformative capability may lie in modelling molecular interactions at atomic resolution — potentially allowing teams to anticipate adverse effects during drug design rather than after patient exposure.
However, this power introduces significant risks. Quantum systems could break modern encryption standards, posing serious threats to data privacy. As innovation accelerates, PV organisations must begin preparing for quantum-resistant encryption and strengthened governance frameworks.
While practical quantum PV applications may be more than five years away, partnerships between pharma and quantum technology firms suggest the foundations are already being laid.
