Kira Radinsky, co-founder and Chairman of Diagnostic Robotics, wants to make healthcare more affordable and accessible. The lessons learned from initial deployments of the startup's AI-based digital triage platform in Israel and the U.S. and the valuable experience gained during the Covid-19 pandemic, point to a future of better healthcare: Providing the right treatment at the right time in the most appropriate setting. At the Mayo Clinic, Diagnostic Robotics' triage platform suggests possible diagnoses and provides a risk score for each patient based on their answers to questions regarding their medical conditions. The Mayo Clinic's Dr. John Halamka calls it "Waze for healthcare," stressing its use as a navigation system, matching patients with the right healthcare resource at the hospital's emergency room or even before they arrive there. The State of Rhode Island has used Diagnostic Robotics' platform to help manage its response to the Covid-19 pandemic.

Can artificial intelligence (AI) predict future events? Successful serial entrepreneur, award-winning inventor, scientist, and technology innovator Kira Radinsky, Ph.D., has an expert's point of view and first-hand experience to answer that question. She is a member of the United Nations Secretary-General's high-level panel on digital cooperation that is chaired by Melinda Gates, Co-Chair of The Bill and Melinda Gates Foundation, and Jack Ma, the Executive Chairman of Alibaba Group. Radinsky is the Co-Founder, Chairwoman, and Chief Technology Officer of Diagnostic Robotics, a health care artificial intelligence (AI) system with predictive analytics, with locations in Tel-Aviv and New York City. In early November 2019, she successfully raised $24 million in Series A venture capital financing led by Accelmed Ventures with other investors for the two-year-old technology startup.

Developing a new drug can cost billions of dollars and take a dozen or more years to bring to market. Two Israeli researchers have applied artificial intelligence (AI) and deep learning to shave time and money off the drug-discovery process. Instead of searching for the appropriate molecules to use in a new medicine, as is done today, they enabled a computer to make smart predictions without human guidance. Shahar Harel and Kira Radinsky at the Technion-Israel Institute of Technology fed into their computer system hundreds of thousands of known molecules as well as the chemical composition of all FDA-approved drugs up until 1950. Aided by AI, the computer came up with new potential molecules by making sometimes unexpected correlations from within this massive sample.

Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precisely labeled causality examples, we mine 150 years of news articles and apply semantic natural language modeling techniques to headlines containing certain predefined causality patterns. For generalization, the model uses a vast number of world knowledge ontologies. Empirical evaluation on real news articles shows that our Pundit algorithm performs as well as non-expert humans.