AI Training for Biotech & MedTech Directors

It's built for people who decide on AI investments in a life sciences organisation: R&D directors, heads of data science, medical affairs and regulatory leads, and the executives who arbitrate their budgets. The assumption is that you already know AI matters in drug discovery and clinical trials, and you need a map of the vendors, the regulatory posture and the ROI, not an introduction to what a neural network is.

The general AI curriculum covers what every executive needs: how models work, where they fail, how to govern them. The biotech and medtech layer applies that to your context, who does what from Insilico Medicine to Tempus, how the FDA and EMA are treating AI-driven models, and where the returns land on a P&L. You get both, and the sector material assumes the general one.

Reading is free and open, no payment and no account required. An account only stores your progress so you can pick up where you left off and keep your assessment results. There's no diploma, no state-recognised certification and no university affiliation attached to it.

The assessment benchmarks your team's AI maturity against peers in the biotech and medtech sector, so the output is a position rather than a score in the abstract. Directors typically use it to identify which capability gaps to close first and to give the board a defensible starting point before committing budget.

Start with the lessons on how the FDA and EMA are actually treating AI-driven models, then move to the playbook to structure your investment case around those constraints. Regulatory posture determines what kind of model documentation and validation you can defend, so it shapes the technical choices rather than following them.

Yes, the playbook is built to be applied to your next AI investment case, and the interactive tools are structured around real biotech decisions rather than illustrative examples. The intent is that you leave with something usable in your next conversation with R&D, legal or the board.