Decoding Gen AI’s Potential to Drive More Effective Clinical Trials

The Syneos Health Podcast

AI is seemingly everywhere now in our lives, including healthcare and drug discovery and development. AI certainly has the potential to enable major advances than development of more effective and safe medical products. And in fact, GenAI has already been successfully used in drug discovery.

But are we fully leveraging the current AI yet in practical ways? Are our trial designs informed appropriately by AI and data? From the mundane document generation and handling to the complex patient engagement, what is the here and now of how we can and should apply AI to support more informed, more effective drug development?

In this episode of the Syneos Health Podcast, Nick Kenny, Chief Scientific Officer at Syneos Health is joined by industry experts Nataraj Dasgupta, Vice President of Advanced Analytics at RxDataScience and Greg Christie, Chief Product Officer at StudyKIK to dive deep into the transformative potential of Artificial Intelligence (AI) in the realm of drug development and clinical trials. The discussion explores the practical applications of AI—from improving patient recruitment and engagement to revolutionizing document generation and protocol design—and touches on the exciting promise of Generative AI (GenAI) in streamlining processes, enhancing patient experiences, and democratizing access to clinical trials. The panel also confronts the challenges, including data accuracy, regulatory concerns, and the ethical implications of AI-driven decisions.

Tune in to understand how AI is reshaping the future of healthcare and what lies ahead in this rapidly evolving field.

For more AI insights from our experts:

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Artificial Intelligence and Alzheimer’s: Transforming Patient Screening with Predictive Models

Syneos Health Podcast | Turbo-Charging Clinical Trials

WEBINAR | How AI is Supercharging Clinical Trials
The views expressed in this podcast belong solely to the speakers and do not represent those of their organization.
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