Rethinking AI for life sciences From retrieval challenges to scalable intelligence pipelines

Why do today’s AI agents still struggle to deliver accurate, decision‑ready answers especially in high‑stakes domains like oncology?

As organizations scale LLM‑powered systems across research, clinical and enterprise environments, a fundamental challenge persists: AI performance is constrained not by the model alone, but by how well complex scientific knowledge is retrieved, structured and contextualized. This becomes especially critical in oncology, where insights must span literature, biomarkers, mechanisms of action, clinical evidence and evolving therapeutic landscapes across the drug value chain.

Critically, AI performance at this level depends on access to high‑quality, curated scientific datasets that provide the depth, coverage, and consistency required for reliable retrieval and reasoning. Built on this foundation, deep ontology integration anchors retrieval and reasoning in scientific meaning, delivering step‑change improvements in AI performance.

In this webinar, we explore the next evolution of AI technology pipelines for scientific and biomedical intelligence. Drawing on real‑world implementations – an oncology-focused application built on TERMite, Abhishek and Birgit will share how combining agentic architectures with deep knowledge structures enables AI systems to reason more effectively over complex, heterogeneous data.

TERMite is Elsevier’s semantic enrichment engine that transforms unstructured scientific text into structured knowledge, enabling AI systems to retrieve, reason and perform more accurately across complex domains like oncology.

This session is designed for leaders and practitioners working at the intersection of AI, life sciences, data platforms and knowledge engineering who want to move beyond model‑centric thinking and understand how robust technology pipelines unlock more reliable, scalable AI outcomes.