Biography
Einat Metzer leads Emedgene, a genomics company using AI to automatically interpret genetic data, so that health organizations can scale personalized care to wider populations. A computer scientist and economist by education, Einat is fascinated by the notion of applying advanced technology and science to realize the vision of personalized care. She co-founded Emedgene and has led its growth from idea to a mature solution used by leading genomics labs including Baylor Genetics, Medical College of Wisconsin and others. Einat is an executive with years of operational experience in product, sales and business development in software companies. Previously Investment Manager with Gemini Israel Ventures, Einat advised portfolio companies on strategy and business development in diverse industries including Enterprise Software and Healthcare IT.

AI and Data Sciences Showcase:
Emedgene
Emedgene provides solutions that interpret genetic tests automatically, helping geneticists manage their growing workload faster and with higher accuracy. This is achieved with an interpretable ML engine that has learned to perform genomics research, after training with millions of data points from patient cases, databases, and the most recent genomics publications. Healthcare organizations using Emedgene reduce time spent on interpretation and reporting and increase yield without increasing headcount, ultimately enabling wide-spread adoption of personalized medicine.

Breaking The Interpretation Bottleneck: Successfully Applying Machine Learning To Genomics
While sequencing is becoming easier, interpreting results of genetic tests is a manual and lengthy process that forms a bottleneck to the adoption of genetic-based care. We’ll cover the complexities applying machine learning to genetic interpretation, and the challenge of keeping up with current genomics discoveries. We’ll also present the results of a study with Baylor Genetics, demonstrating the accuracy and utility of an AI solution in clinical use.