Privacy Challenges in the Health Sector through Emerging Technologies
Integration of artificial intelligence biomarkers in clinical decision making
Precision oncology requires molecular and genetic testing of tumor tissue. For many tests, universal implementation in clinical practice is limited because these biomarkers are costly, require significant expertise and are limited by tissue availability. However, virtually every cancer patient gets a biopsy as part of the diagnostic workup and this tissue is routinely stained with hematoxylin and eosin (H&E). Recently, we and others have demonstrated that deep learning can infer tumor genotype, prognosis and treatment response directly from routine H&E histology images. This talk will summarize the state of the art of deep learning in oncology, demonstrate emerging use cases and discuss the clinical implications of these novel biomarkers. In addition, the talk will provide use cases for integration of AI-based biomarkers in medicine beyond oncology.
Jakob Nikolas Kather is an assistant professor at RWTH Aachen in Germany. In addition to his clinical activities in gastrointestinal oncology, he leads a research group focused on artificial intelligence biomarkers. His work on deep-learning-based biomarkers in solid tumors has recently been published in Nature Medicine and Nature Cancer. Learn more about his research on www.kather.ai.