Precision oncology holds immense promise but presents a staggering computational and clinical challenge. Each tumor typically harbors 4 to 5 actionable driver alterations, chosen from millions of potential combinations across more than 700 cancer-related genes. The traditional approach—generating high-level clinical evidence through randomized clinical trials for every unique genetic profile—is simply not scalable. In fact, it would take an estimated 14.4 million years to complete such trials for all possible combinations. In this presentation, Dr. Istvan Petak will explore how next-generation clinical decision support systems are reshaping this paradigm. These systems leverage deterministic, multidimensional algorithms to synthesize vast genomic, clinical, and real-world evidence in real time. The result? A scalable, evidence-based strategy for personalizing treatment plans, one that bypasses the time constraints of conventional research while upholding clinical rigor.
Learning Objectives:
1. Discuss the concept of precision oncology
2. Discuss the dimensionality problem of precision oncology
3. Discuss the use of different types of AI in precision oncology
