AI in pharma
Where AI genuinely helps across the pharma value chain, from drug discovery to pharmacovigilance, and the regulatory reality that shapes what you can actually deploy.
AI is reshaping pharmaceuticals across drug discovery, clinical trials, manufacturing and commercial operations, but the sector's regulatory intensity, safety obligations and data complexity demand a distinct approach to adoption. This block builds fluency in how core AI concepts translate into pharma-specific applications, from molecule screening to trial patient matching to pharmacovigilance signal detection. You will learn to evaluate where AI creates genuine value across the value chain versus where hype outpaces evidence, how to assess vendor claims and ROI realistically, and how to navigate the governance, model risk and regulatory frameworks (FDA, EMA) that govern AI use in a highly regulated, patient-safety-critical industry. The goal is practical judgment, not technical depth.
What you'll master
- Map AI use cases across the pharma value chain from R&D to commercialization and identify where they generate real ROI
- Critically evaluate AI vendor claims and pilot results using sector-appropriate evaluation criteria
- Identify key regulatory requirements (FDA/EMA) and governance structures needed before deploying AI in clinical or commercial settings
- Recognize major AI risks specific to pharma, including model bias in trials, data privacy and hallucination in pharmacovigilance, and apply guardrails to mitigate them