Insight on AI – March 2026, Issue 8
Welcome to the March, Issue 8 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 and Human Organoids Could Redefine Drug Safety Before Trials Begin
Drug development remains a high-risk process, with nearly 90% of drugs failing during clinical trials. One of the most persistent challenges is drug-induced liver injury (DILI), a leading cause of late-stage failure.
A new wave of innovation is combining AI with human-derived organoids — miniature, lab-grown tissues that closely replicate human biology. Unlike traditional animal models, these systems can metabolise drugs and respond to toxicity in ways that more accurately reflect human physiology.
The results are striking. AI-powered platforms can process tens of thousands of toxicity tests in a single day, achieving significantly higher predictive accuracy than traditional methods that take months.
This approach signals a broader shift toward computational, human-relevant drug safety assessment. By enabling earlier and more accurate toxicity prediction, it has the potential to reduce costly late-stage failures, accelerate development timelines and improve patient safety.
While regulatory validation and scalability remain challenges, the direction is clear: drug safety may increasingly be assessed through biological simulation rather than approximation.
AI News
AI is Becoming Healthcare’s First Point of Influence — But That Comes with Risk
AI is rapidly becoming the first place people turn for healthcare information. Millions of health-related queries are now directed to AI systems each week, many of them complex, personalised and time-sensitive.
Unlike traditional search engines, AI does more than retrieve information — it interprets, summarises and prioritises it. In doing so, it effectively shapes the narrative before users engage with original sources. This shift represents a fundamental change in how healthcare information is consumed and understood.
However, this growing influence introduces significant risks. AI outputs depend heavily on the data they are trained on. When information is incomplete or biased, models may generate oversimplified or inaccurate insights. Overreliance is another concern, as users may begin to trust summarised outputs without questioning their limitations.
For pharmaceutical organisations, this creates a new responsibility. Communications are no longer just for human audiences — they also shape the data environment that AI learns from. Ensuring consistent, high-quality and accessible information is now essential to maintaining accuracy, trust and balanced representation in AI-driven healthcare narratives.
AI can accelerate access to knowledge, but it cannot replace clinical judgement, scientific nuance or contextual understanding.
Industry
AI is Redefining How Medical Affairs Engages Healthcare Professionals
Medical Affairs is becoming increasingly central to pharmaceutical strategy, with rising expectations around the depth, consistency and measurable impact of scientific exchange. The role of Medical Science Liaisons (MSLs) is evolving rapidly, as healthcare professionals demand more tailored, evidence-driven conversations on complex clinical topics.
AI is now emerging as a powerful enabler in this space. Rather than relying on static training methods, new AI-driven approaches are introducing continuous, real-world learning. Simulation platforms allow MSLs to engage in realistic, adaptive interactions with virtual healthcare professionals, reflecting diverse communication styles and levels of expertise.
The real value lies not just in realism, but in measurable improvement. Each simulated interaction generates structured feedback, enabling individuals to refine their communication strategies while giving organisations visibility into performance trends and capability gaps.
Importantly, AI is not replacing expertise — it is amplifying it. As Medical Affairs continues to scale, AI will play a critical role in ensuring high-quality, consistent scientific engagement that ultimately supports better clinical understanding and patient outcomes.
Innovation
Synthetic Data: Safely Accelerating AI with Simulated Patients
As AI adoption grows, access to high-quality data remains one of the biggest constraints — particularly in a field where patient privacy is paramount. Synthetic data is emerging as a practical solution.
Synthetic datasets are artificially generated but statistically mirror real-world patient data. In pharmacovigilance, this enables organisations to create realistic adverse event cases, electronic health records or even entire safety databases without exposing sensitive information.
This opens new possibilities for AI development. Rare safety signals — often difficult to capture in real datasets — can be embedded into synthetic data, allowing models to be tested and validated more effectively. Synthetic case narratives can also enhance natural language processing systems by exposing them to varied descriptions of adverse events.
The concept is still evolving, and challenges remain. Synthetic data must maintain high fidelity, preserving real-world relationships between drugs and outcomes. Poorly constructed datasets risk misleading AI systems rather than improving them.
Despite this, momentum is building. Synthetic data is increasingly seen as a “sandbox” for innovation — enabling experimentation, collaboration and model development without compromising patient confidentiality.
In the coming years, it is likely to play a critical role in accelerating pharmacovigilance analytics, where data access has historically been a limiting factor.
