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Building AI-Ready Cultures in Life Sciences R&D - with Xiong Liu of Novartis
Manage episode 514915100 series 79750
Today's guest is Xiong Liu, Director of Data Science and AI at Novartis. Novartis is among the world's leading pharmaceutical companies, pioneering data and advanced analytics in the pursuit of new medicines and patient outcomes. Xiong joins Emerj Editorial Director Matthew DeMello to examine how generative AI and foundation models are transforming R&D, clinical workflows, and research collaboration across the life sciences. The discussion highlights how domain-specific data strategies, improved data quality, and shared benchmarks are accelerating discovery and operationalizing AI for measurable ROI in biopharma. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!
1037 episodes
Manage episode 514915100 series 79750
Today's guest is Xiong Liu, Director of Data Science and AI at Novartis. Novartis is among the world's leading pharmaceutical companies, pioneering data and advanced analytics in the pursuit of new medicines and patient outcomes. Xiong joins Emerj Editorial Director Matthew DeMello to examine how generative AI and foundation models are transforming R&D, clinical workflows, and research collaboration across the life sciences. The discussion highlights how domain-specific data strategies, improved data quality, and shared benchmarks are accelerating discovery and operationalizing AI for measurable ROI in biopharma. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!
1037 episodes
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