Your people are already using AI. They’re writing code with it, analyzing data with it, drafting regulatory documents with it – with or without your governance frameworks, your ontology standards, or your validation processes. The question isn’t whether to enable this; it’s whether you’re going to lead it or clean up after it. The Pistoia […]
AI is rapidly moving from promise to practical application in clinical development. This session will explore how advances in AI, patient engagement, in-silico approaches and digital twins are reshaping the design and delivery of clinical trials. Bringing together perspectives from across the research and innovation ecosystem, speakers will share emerging approaches to AI-enabled clinical […]
For decades, the standard response to a research bottleneck has been simple: “We need more data.” But in today’s landscape, the limiting factor is rarely a lack of information—it is the fragmentation of the data we already own. Join info pro Mary Ellen Bates and Mark Hahnel (Digital Science) for a discussion on “The […]
Includes the 2026 series (8 live and/or on demand sessions), plus on-demand access to the 2025 series of 9 sessions. Building on the strong foundation established in the 2025 Training Series, the 2026 Training Series represents the next stage in the AI learning journey for pharmaceutical professionals. The 2025 curriculum was developed through […]
This end-of-summer seminar takes a longitudinal view of AI across the full drug development pipeline — from the earliest stages of target identification and validation through to clinical trial design and execution. Through in-depth case studies and expert presentations, participants will examine where AI is delivering genuine, measurable value and where significant challenges remain. […]
In an industry where failure is rarely discussed openly, this round table offers something genuinely valuable: a safe, pre-competitive space to share what has gone wrong with AI projects — and what was learned as a result. From misaligned expectations and poor data quality to underestimated complexity and organisational resistance, AI initiatives fail for […]
Building on the AI-Ready Data Workshop held at the Boston Conference, this follow-up session reports back on the discussions, consensus, and open questions that emerged including how FAIR data principles map to AI readiness, and what additional dimensions of data quality the community identified as critical. As with all LSAIE round tables, numbers are […]
A year-end look back at how the field of AI in life sciences has evolved over 2026, featuring reflections from returning LSAIE contributors alongside forward-looking perspectives on priorities for 2027. This session also closes the loop on the AI-Ready Data initiative for members of the wider community. Register
