Microsoft announces AI-based healthcare initiatives

21 Sep 2016 | By Shiladitya
Microsoft's new quest to end cancer

Microsoft announced a quartet of healthcare initiatives to "solve" cancer using algorithms and computer science rather than beakers and test tubes.

The four projects are mainly centred around the use of artificial intelligence in a bid to understand cancer and subsequently find a cure to it.

In context: Microsoft's new quest to end cancer

The projectsThe four healthcare projects announced by Microsoft

One of Microsoft's teams is using machine learning to figure out individualized, effective cancer treatments.

The second team is using machine learning and computer vision to provide a detailed understanding of the progress of tumours.

Another team is using algorithms to figure out how cancers develop.

Meanwhile, the fourth team is working towards a future in which cells could be programmed to fight diseases.

21 Sep 2016Microsoft announces AI-based healthcare initiatives

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Computers and Biology

Speaking on the link between computational processes and biological processes, Andrew Phillips, the head of Microsoft's Cambridge biological computation research lab said, "We can use methods that we've developed for programming computers to program biology, and then unlock even more applications and even better treatments."
The approaches taken in Microsoft's projects

ApproachesThe approaches taken in Microsoft's projects

Microsoft's quest to end cancer using computer science has two basic approaches.

One assumes that biological processes, including cancer, are information processing systems and thus applies tools used to reason and model computational processes to biological ones.

The other is more data-centric, and it proposes to use machine learning to analyze the huge amount of available biological data to better understand and treat cancer.

IBM's programme to end cancer

Akin to one of Microsoft's projects, IBM is working on a programme called Watson Oncology which will use computer science to sift through 1.5 million patient records and 600,000 medical records to come up with more effective cancer treatments.