Healthcare Videos

Generative AI in the Real World: Danielle Belgrave on Generative AI in Pharma and Medicine

In this podcast, GSK’s Vice President of AI and Machine Learning, Danielle Belgrave, explains how generative and traditional AI are being deployed across the pharmaceutical pipeline. Drawing on a 15‑year career that spans a PhD on asthma heterogeneity, stints at Microsoft Research and DeepMind, and now leading AI for clinical development at GSK, she outlines the shift from one‑size‑fits‑all treatments to data‑driven, patient‑specific interventions. Belgrave highlights several use cases: leveraging whole‑genome and RNA‑seq data to inform computational pathology, using multimodal foundation models to translate molecular signals into biopsy image insights, and employing generative AI to synthesize large‑scale multi‑omics datasets for target identification. She also stresses the importance of responsible AI, noting GSK’s dedicated team that applies model cards, external reviews, and hallucination metrics to ensure safety and reproducibility. Concrete examples include her PhD work that identified five distinct asthma subtypes, current projects that map genomic profiles onto tissue‑level pathology, and internal language models such as “jewels” that assist scientific productivity while being rigorously vetted. The discussion underscores the breadth of data—clinical notes, biomarkers, microbiomes, epigenetics—and the technical hurdles of batch effects, small‑sample robustness, and data sparsity. The overarching implication is that AI, especially generative and multimodal models, can dramatically shorten drug discovery cycles, improve trial enrollment efficiency, and enable precision therapeutics, provided that robust governance and methodological safeguards keep pace with rapid innovation.