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What are the prospects for applying AI to improve healthcare? Three types of problems that AI can address in healthcare will be outlined, the most challenging of which is the development of new therapeutics. To address this challenge, we leverage recent advances in machine learning and high-throughput experimentation to apply the engineering cycle to drug discovery. The engineering cycle is based on iteratively measuring a system, modeling it computationally, and manipulating it. Each time the cycle is completed, the results improve. This iterative approach is fundamental to all engineering design but, until now, has had limited impact on drug discovery. Progress on unpublished projects relating to these efforts will be described, including a collaborative, multi-institutional project called Answer ALS.
Brad Pentelute
Introducing Analog Devices’ Digital Health business and the role of sensors in Medtech Brendan Cronin Director, Digital Healthcare Group at Analog Devices
Peek into research
Rapid Antigen Diagnostics for Emerging Pathogens Lee Gehrke Hermann L.F. von Helmholtz Professor of Health Sciences GI device development in a few movements Giovanni Traverso Assistant Professor, Mechanical Engineering Electronic Textile Conformable Suit (E-TeCS) Canan Dagdeviren LG Career Development Professor of Media Arts and Sciences at MIT Media Lab MR relaxometer for improving clinical outcomes in hemodialysis Michael Cima David H. Koch Professor of Engineering, MIT Koch Institute for Integrative Cancer Research
Chuchu Fan