Early Signals at ASCO 2026 | AI, Multimodal Data and the Next Era of Precision Oncology

What if artificial intelligence could help remove the uncertainty from cancer treatment decisions?

Precision oncology has already transformed cancer care by matching therapies to actionable biomarkers, but today's advances are moving beyond genomics alone. By integrating molecular, clinical and pathology data at scale, AI is helping researchers and clinicians better predict treatment response, identify the right patients for therapies and accelerate the development of more personalized cancer care.

In this episode of The Syneos Health Podcast: Early Signals, Wael Harb, MD, Head of R&D and Scientific Strategy in Oncology at Syneos Health, sits down with Ezra Cohen, MD, Chief Medical Officer at Tempus AI, to discuss how multimodal data and artificial intelligence are reshaping precision oncology. Together, they explore the evolution of biomarker discovery, the growing role of AI in clinical decision-making and what it will take to bring truly individualized cancer care into routine practice.

The conversation also explores how digital pathology, real-world data and AI-enabled clinical workflows are improving patient selection, supporting guideline-directed care and laying the foundation for earlier detection and more precise treatment strategies.

Topics include:

• Why precision oncology extends beyond targeted therapies to reducing uncertainty throughout the patient journey
• How multimodal data and AI are improving biomarker discovery and treatment selection
• The promise of digital pathology and AI-powered clinical decision support
• Using real-world data to improve immunotherapy response prediction
• Closing care gaps through AI-enabled clinical workflows
• The future of minimal residual disease, early detection and personalized cancer treatment
• Why collaboration and data sharing will be essential to unlocking AI's full potential in oncology

The views expressed in this podcast belong solely to the speakers and do not necessarily represent those of their organizations.