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About the Guest
Caroline Chung
Vice President and Chief Data & Analytics Officer, MD Anderson Cancer Center
With over two decades of experience in radiation oncology, quantitative imaging, and data science, she operates at the intersection of AI, digital twins, and precision medicine. She leads enterprise-scale initiatives focused on building data ecosystems, AI-driven platforms, and clinically meaningful innovations that improve patient outcomes and accelerate discovery. Her work spans predictive modeling, imaging analytics, and digital twin technologies, shaping the future of personalized cancer care and data-driven healthcare transformation.
Brief About the Episode
In this episode of TeqTalk, Jas Kaur speaks with Caroline Chung to unpack the realities of AI adoption in oncology, where every data point represents a patient and data fragmentation, lack of standardization, and system complexity create significant barriers to reliable outcomes. From unifying over 200 clinical protocols in record time to building a metadata-driven data ecosystem, Caroline shares how leading institutions are balancing innovation with accountability while advancing digital twins, clinical trial simulation, and precision medicine in one of the most high-stakes environments in healthcare.
Key Learnings for Leaders
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AI in Healthcare Is a Risk Management Problem, Not Just a Technology Problem
Unlike other industries, AI failures in oncology can lead to delayed diagnosis, incorrect treatments, and loss of trust. Leaders must approach AI with clinical accountability, not just technical ambition.
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AI Must Augment, Not Replace, Human Judgment
Automation can process scale, but clinical decisions require context, interpretation, and human oversight. Overreliance on AI introduces risks like automation bias and skill degradation.
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Digital Twins Are Redefining the Future of Oncology
From simulating clinical trials to predicting treatment responses, digital twins are unlocking new possibilities—but they demand high-precision data and system-wide consistency.
Building AI in healthcare isn’t just about deploying models, it’s about designing systems that can be trusted when the stakes are highest.
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