Greetings!
There is no shortage of talk about AI in pharma.
The harder question is: what is it actually doing?
Big Data and AI are already changing parts of drug discovery, clinical trials, and safety. But the picture is far from uniform. Some applications have real evidence behind them. Others are still largely experimental. And as the technology moves further into regulated work, another question becomes increasingly important: what do regulators actually expect?
Proof, Not Promise: How AI Is Actually Changing Pharma is built around that distinction.
Rather than another broad conversation about what AI might eventually do, this workshop looks at where things actually stand today, what the evidence tells us, and where the regulatory landscape is heading next.
We’ll get into:
- How Big Data and AI have changed some of the basic assumptions behind pharma R&D
- Where AI is actually making a difference across discovery, clinical trials, and safety
- What has been proven so far, and what hasn’t
- Where FDA, EMA, and ICH currently stand on AI
- How “context of use” and risk can help you make sense of AI tools and claims
- What to keep an eye on over the next 12-18 months as regulation continues to evolve
The point is not to come away believing that AI will change everything.
It is to come away with a much clearer sense of what has already changed, what deserves some skepticism, and what people working in pharma should be paying attention to now.
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