JUN 21, 2018 09:00 AM PDT

Can AI Beat Cancer?

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  • Founder and Chairman at Cancer Commons
      Dr. Jay M. ("Marty") Tenenbaum, a world-renowned Internet commerce pioneer and visionary, founded CommerceNet (1994) to accelerate business use of the Internet. While at CommerceNet, he co-founded Veo Systems (1997), the company that pioneered the use of XML for automating business-to-business transactions. When Commerce One acquired Veo Systems in January 1999, Dr. Tenenbaum became chief scientist and was instrumental in shaping the company's business and technology strategies for the Global Trading Web.

      Post Commerce One, Dr. Tenenbaum was an officer and director of Webify Solutions (sold to IBM in 2006) and Medstory (sold to Microsoft in 2007). Currently, his focus is on transforming healthcare and accelerating therapy development through collaborative e-science. Towards that end, he founded CollabRx, which builds "virtual biotechs" to help slash the time, cost, and risk of developing new therapies."

      Prior to CommerceNet, he was founder and CEO of Enterprise Integration Technologies, the first company to conduct a commercial Internet transaction (1992), secure Web transaction (1993), and Internet auction (1993). Earlier in his career, Dr. Tenenbaum was also a prominent AI researcher, and led AI research groups at SRI International and Schlumberger Ltd.

      Dr. Tenenbaum is a fellow and former board member of the American Association for Artificial Intelligence, and a former consulting professor of Computer Science at Stanford. He currently serves as a director of Efficient Finance, Patients Like Me, and the Public Library of Science, and is a consulting professor of Information Technology at Carnegie Mellon's new West coast campus.

      Dr. Tenenbaum holds B.S. and M.S. degrees in Electrical Engineering from MIT, and a Ph.D. from Stanford.


    Finding effective treatments for cancer is fundamentally  a high-dimensional probabilistic planning, search, and optimization problem, characterized by  thousands of molecular subtypes,  tens of thousands of plausible drug regimens, and a dearth of high quality data on clinical responses.  These explosive combinatorics and the lack of data are overwhelming traditional approaches to clinical development, including the latest Bayesian adaptive trial designs and big data analytics.  However, this is precisely the kind of knowledge-based  planning and search problem that AI is uniquely designed to solve.  Many organizations are developing relevant AI-based applications, from decision support and treatment planning to treatment validation and real time response monitoring.  We will review these developments and describe a bold industry-wide initiative to integrate them for the immediate benefit of all cancer patients and their physicians, while radically improving the efficiency of clinical research and drug development. 

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