AI moves deeper into pharma and life sciences operations
Artificial intelligence is moving from pilot projects to core operations across the pharmaceutical and life sciences industry, with implications that increasingly reach animal health, diagnostics, manufacturing, pharmacovigilance, and regulatory work. A recent PharmaShots overview by Rahul Mittal frames AI as a technology reshaping how medicines are discovered, developed, manufactured, approved, and supplied, and that broader direction is now backed by regulators and industry analysts. The FDA has published a revised discussion paper on AI use in drug and biologic development and, in early 2025, issued draft guidance for AI models used to support regulatory decision-making in submissions. In Europe, the EMA has folded AI into its 2025-2028 data strategy and says its principles apply to both human and veterinary medicines. At the same time, PharmaShots reporting on genomics company Inocras offers a more concrete example of what this looks like on the ground: AI-driven bioinformatics being used to turn whole-genome sequencing from a research tool into a more standardized clinical workflow, with platforms aimed at oncology, rare disease, and longitudinal monitoring. (fda.gov)
Why it matters: For veterinary professionals, the immediate takeaway isn't that AI is replacing clinical or regulatory judgment. It's that AI-enabled tools are becoming part of the evidence, manufacturing, safety, and information systems that shape which therapies reach the market, how reliably they're supplied, and how post-market signals are monitored. That matters in companion animal and livestock care alike, because the same governance questions seen in human pharma, including model credibility, data quality, transparency, and human oversight, are directly relevant to veterinary therapeutics and diagnostics. The Inocras example also highlights a second point: AI's value often depends on whether it can make complex data, such as genomics, usable in routine clinical workflows rather than just generating more information. Regulators are signaling support for AI, but only within risk-based frameworks. (fda.gov)
What to watch: Watch for more concrete guidance on good AI practice, additional regulatory use cases in medicine development, and clearer proof that AI can improve real-world R&D, manufacturing, safety, and clinically actionable decision support, not just efficiency claims. Examples to follow include whether AI-driven genomic platforms can consistently deliver standardized, cost-effective insights at scale in real care settings. (ema.europa.eu)