AI-driven pharmacovigilance has spent the past two years chasing model accuracy, on the assumption that better performance would translate directly into lighter workloads. Jason Bryant,…
The international popularity of GLP-1 agonists for weight loss has presented an unforeseen operational challenge for pharma as companies try to keep track of emerging…
The overall architecture of pharmacovigilance (PV) has changed very little over the last couple of decades. As AI-driven process automation advances from theoretical possibility to…
The life sciences industry has spent the better part of a decade modernising its regulatory infrastructure — investing in global regulatory information management (RIM) systems,…
Lately, there has been a lot of talk about the potential use cases for—and benefits of—artificial intelligence (AI) for the pharmaceutical industry. Compared with other…
The increasing urgency and frequency of emerging infectious diseases necessitates faster, more agile and scalable vaccine solutions.1 While the rapid spread of health threats is…
In 2026, setting clear AI expectations is the priority. On January 14, 2026, EMA and FDA marked a step towards regulatory alignment by publishing joint…
As the medtech industry evolves, companies are facing resource limitations, the burden of manual processes, greater clinical evidence demands, and site collaboration challenges. For medtechs…
Clinical trials are fundamentally changing the way patient experience data is captured. In a time when regulators are putting much greater emphasis on patient-focused drug…
Key Points AI chaos is a success-driven challenge: Powerful, specialized AI tools are proliferating faster than labs can integrate them coherently. Fragmentation creates hidden costs:…
