Insight on AI – June 2026, Issue 13
Welcome to the June, Issue 13 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.
Innovation
World’s First AI Vaccine
Researchers at the University of Cambridge have successfully tested the first AI-designed vaccine antigen in humans. Rather than targeting a single virus strain, the vaccine was designed using genetic information from multiple coronaviruses to create broader protection across an entire viral family.
The goal is to develop vaccines capable of protecting against current and future coronavirus threats, including those that have yet to emerge in humans. Early trials involving 39 participants focused primarily on safety and demonstrated the potential of AI-assisted vaccine design.
The same technology is now being explored for universal influenza vaccines, bird flu and Ebola. While larger trials are still underway, the research highlights AI’s growing role not just in analysing data, but in designing entirely new medical interventions.
AI News
AI has its Foot on the Gas Pedal, but does it need a Brake?
Anthropic co-founder Jack Clark has warned that AI may be advancing faster than society’s ability to govern it. Speaking to BBC Newsnight, he argued that while the industry has a “gas pedal,” it lacks a “brake pedal” to slow development if risks begin to outweigh benefits.
Clark highlighted concerns around increasingly autonomous AI systems, revealing that around 80% of Claude’s code is now AI-generated. He also warned of economic disruption as AI takes on more routine work.
Despite these concerns, Clark remains optimistic that human creativity, curiosity and strategic thinking will become even more valuable. His broader message is that innovation must be matched by equally strong investment in regulation, oversight and accountability.
Tech News
Closing the Pregnancy Evidence Gap with AI
Pregnant women have historically been excluded from clinical trials, leaving major gaps in our understanding of medicine safety during pregnancy. Researchers are now using AI and large healthcare datasets to help generate evidence where traditional studies have struggled.
Projects such as BOOST-HP and BIONIC combine machine learning with causal inference methods to identify potential links between medicines and maternal or foetal outcomes. However, researchers stress that AI cannot determine causality on its own and must be used alongside established epidemiological approaches.
If successful, these methods could support safer prescribing decisions and improve outcomes for mothers and babies, while helping address one of medicine’s longest-standing evidence gaps.
