The Technion and Rambam Health Care Campus hosted a joint conference in February on "Machine Learning in Medicine"
Rambam Health Care Campus and the Technion-Israel Institute of Technology hosted a joint conference on "Machine Learning in Medicine" in the Spencer Auditorium, located in the Sammy Ofer Tower at Rambam, on Thursday, February 22, 2018.
The conference highlighting cooperation between the Technion and Rambam was introduced by Technion President Professor Peretz Lavie, and Rambam director Professor Rafi Beyar who declared “The bond between Rambam and the Technion with the Ruth and Bruce Rappaport Faculty of Medicine on our clinical campus is a unique opportunity. Each faculty has an interest in medicine. We at Rambam generate huge amounts of data through our electronic medical records, imaging equipment, pathological data, and genetic information. This presents an outstanding opportunity for research and for machine learning and artificial intelligence studies.”
Technion President Professor Peretz Lavie described the stormy discussions that preceded the establishment of the Ruth and Bruce Rappaport Faculty of Medicine at the Technion: “Today there is no doubt that the Technion is the right place for medical research related to engineering, science and technology, but back in 1969, the decision to establish the medical faculty was preceded by stormy discussions and significant objections. In the end, the decision was made in the Senate with the understanding that in the future, technology and medicine would go hand in hand. Most of the Technion Senate members who made the decision are no longer with us, but they would have certainly been surprised by the number of participants here at the conference. I have no doubt that in their wildest dreams they could not have guessed how close the relationship between technology and medicine would be.”
The organizers of the conference, Professor Yehoshua Y. (Josh) Zeevi and Professor Shie Mannor of the Technion's Andrew and Erna Viterbi Faculty of Electrical Engineering, explained the program is designed to create collaborations between physicians and data and information engineering researchers in order to develop tools for early diagnosis of diseases. “In the not too distant future,” Professor Mannor observes, “Our lives will be documented and monitored continuously from our nutritional habits and physical activity to the quality of our sleep. Each person will know what medical tests they need and when, just like we make routine car maintenance appointments. This will democratize the field of medicine—power will be transferred from the physician to the patient.” Professor Zeevi predicts, “Learning systems have already started to revolutionize medicine in the world. Cooperation between Rambam and the Technion will place Israel in a leading position in the field of machine learning, as our start-up nation leads in hi tech.”
Professor David Sontag from MIT, Professor Suchi Saria of John Hopkins, Dr. Kira Radinsky founder of SalesPredict and visiting professor at the Technion, Technion's Professor Emeritus Yehoshua Zeevi, and Professor Shie Mannor from the Technion's Andrew and Erna Viterbi Faculty of Electrical Engineering spoke to an overflowing audience of intellectual and entrepreneurial powerhouses, and medical and business persons from Israel and worldwide, including Intel, IBM, Dell EMC, Elbit Systems, GE Medical, and more—illustrating the excitement, interest and expectations for new breakthroughs in the diagnoses and treatment of patients.
Professor Beyar concluded, “This is not science fiction, these are real research directions that will penetrate every corner of medicine, whether it is physiologic, biochemical, pathological, imaging, molecular or genetic data…artificial intelligence will make health care more efficient, eliminate unnecessary tests, reduce time to diagnosis and treatment, lead to precise and efficient medicine, improve the flowchart for disease diagnosis and treatment, and improve access to health care.”
At the end of the conference, a panel of experts was held under the guidance of Professor Gabriel Barbash from the Weizmann Institute on the use of machine learning technologies—potential, impact and challenge.
Biosketches of Presenters
Dr. Kira Radinsky completed three degrees at the Technion's Faculty of Computer Science and is currently a Visiting Professor at the Technion. During her graduate studies Dr. Radinsky developed a methodology for predicting future events based on Internet queries. Based on technologies developed during her studies, she founded SalesPredict, which was later acquired by eBay. The company predicts the probability of selling products and services for client companies. As a result of the acquisition, Dr. Radinsky was appointed as eBay's Chief Scientist in Israel. In collaboration with Israeli health care providers, she develops methods for predicting medical problems based on the full range of information available in Israel's personal medical databases and medical literature.
Professor David Sontag is a faculty member of MIT's Institute for Medical Engineering, and thee Science Department of Electrical Engineering and Computer Science, where he completed his undergraduate and graduate degrees; subsequently doing his post-doctorate studies at Microsoft Labs. He currently focuses on the study of artificial intelligence and its use in the medical field.
Professor Suchi Saria of Johns Hopkins University is involved in a wide range of fields, including artificial intelligence and computational statistics and their applications in time varying systems. Over the past seven years, she has focused on computational solutions in medical informatics and the development of "early warning systems" in medicine, especially the monitoring of infection (sepsis). In her words, “Artificial intelligence is already saving lives today by significantly shortening the speed of diagnosis. The key is to know what to treat, when to treat, and how to treat, and that information is in the data.”
Professor Shie Mannor from the Technion's Andrew and Erna Viterbi Faculty of Electrical Engineering completed a dual undergraduate degree with honors (Electrical and Mathematical Engineering, 1996) before serving four years as an intelligence officer in the Israel Defense Forces, and then working in high-tech. In 2002, he completed his doctorate in electrical engineering at the Technion under the guidance of the current Dean of the Faculty, Professor Nahum Simkin. Afterwards he was a Fulbright postdoctoral associate at MIT until 2004, held a Canada Research Chair in Machine Learning at McGill University, then joined the Technion's Andrew and Erna Viterbi Faculty of Electrical Engineering.
Professor Yehoshua Y. (Josh) Zeevi completed a bachelor's degree in electrical engineering from the Technion, a master's degree from the University of Rochester in New York, and a PhD. from the University of California, Berkeley. Already while pursuing his bachelor's degree, Professor Zeevi began research in bio-electronics under the guidance of the legendary professor Franz Ollendorff. After completing his doctorate, he served as a visiting scientist at the Lawrence Berkeley National Laboratory and later was a fellow in the Applied Science Department at Harvard and a researcher in the MIT's program in Brain Science (MIT-NRP). Upon returning to the Technion as a member of the Faculty of Electrical Engineering, he founded the Vision and Image Sciences Laboratory, focusing on aspects of biological and computer vision, neural networks, image processing and identification algorithms that mimic the natural vision system; later establishing the Ollendorff Minerva Center for Vision and Image Sciences. Over the years he has served in many management positions at the Technion, including as Dean of the Faculty of Electrical Engineering.
Professor Zeevi is a partner in more than 100 patents and has founded many companies—the best known of which is Cortica, founded in 2007 based on a study that examined the neural tissue in the cortex responsible for vision. Zevi's scientific research led to the realization that the number of brain layers have not significantly increased during evolution. The architecture and algorithms developed by Cortica, based on this research, led to the advanced processing of natural signals such as speech and radar signals, images and searches in big data and in various applications such as medical data analysis and computer vision systems for autonomous vehicles.