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The healthcare sector, in spite of using cutting-edge technology, is also crucially traditional. For decades, the procedure has been the same, with a focus on curing diseases when they appear. However, changes are finally on sight.

The first one is the emerging focus on disease prevention. An example is MyLevels’ use of glucose monitors, to see the impact of food on your body (Mylevels.com, 2018). Although its main application is to promote healthier nutrition, the technology also has application for diabetes treatments. But the technology can go even further, with sensors being implanted into the body itself. Digestible nano-sensors could provide real-time information regarding the patient’s health, both to his doctor and to himself (Rothkopf, 2016). This is especially empowering for patients, in situations where they are often left helpless and crucially uninformed. Such technology not only has the potential to change the way we treat our bodies, but also to change the amount of control we have over them.

A different technology worth mentioning is 3D printing. Having a part of you removed is traumatic enough; yet patients also have to deal with a scarce amount of donors. That is a problem 3D printing is pledged to solve: thanks to the incredible broad range of possibilities, this technology can produce medical prosthetics, implants, and even organs (CB Insights Research, 2018). And its full potential is still to be seen: the first 3D printed kidney transplant dates back only to 2016 (Murgia, 2018), so one can imagine the possibilities 10 years from now.

Finally, another impactful technology is drone delivery. As my classmate Nicole mentioned in her article -you can find the link to her article below-, drones  in Rwanda deliver blood samples to dedicated hospitals, leading to faster treatment. Seeing how companies like Amazon use drones for deliveries, it is possible we will witness the rise of drones delivery, not only to hospitals, but to our offices, homes.

It might seem like these technological breakthroughs are unrelated -they indeed all tackle very different health issues. But the truth is that they have been enabled by a quite recent technology -namely AI. Investments in healthcare related tech -and AI- have increased exponentially, with large “hub” firms turning their heads to this industry, hoping to get the largest piece of the pie. See the venture of Amazon, JP Morgan & Berkshire Hathaway, but also investments from Apple, Google, and many others. With so many giants preparing to compete, it will be interesting to witness how the future of healthcare will look like -possibly with a shift from a public service, to a private one.

Nicole’s article: https://digitalstrategy.rsm.nl//2018/09/30/how-drones-are-saving-lives-in-rural-rwanda/

Bibliography:

CB Insights Research. (2018). From Construction To Art, Here Are 25 Industries That 3D Printing Could Disrupt. [online] Available at: https://www.cbinsights.com/research/report/3d-printing-technology-disrupting-industries/?utm_source=CB+Insights+Newsletter&utm_campaign=ba91154b4a-Top_Research_Briefs_09_01_2018&utm_medium=email&utm_term=0_9dc0513989-ba91154b4a-89733553 [Accessed 8 Oct. 2018].

Murgia, M. (2018). Toddler gets world first adult kidney transplant using 3D printing. [online] The Telegraph. Available at: https://www.telegraph.co.uk/technology/2016/01/26/toddler-gets-world-first-adult-kidney-transplant-using-3d-printi/ [Accessed 8 Oct. 2018].

Mylevels.com. (2018). myLevels: tap into your body. [online] Available at: https://mylevels.com/ [Accessed 8 Oct. 2018].

Rothkopf, D. (2016)., The Great Questions of Tomorrow

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Of all the technologies currently developped, few have been scutinized as closely as AI. Indeed, while the previous Industrial Revolution was about automating human tasks, the next one is likely to see machines making decisions in our places. The development of machine learning refers to a computer software that seeks to reproduce or mimic (perhaps with improvements) human thought, decision making, or brain functions (Gallaugher, 2016). This learning is conducted by feeding AIs with millions of examples labelled with the right answer. But what would happen if the wrong answer was labelled right -if the learning went wrong?

That is the question Dr Yanardag tried tackling. Instead of regular data sets, she exposed her algorithm -nicknamed Norman, after the eponymous murderer in Hitchcock’s Psycho- to the darkest corners of Reddit. Norman was then presented with a picture, for which he should find a caption. While standard AI system would label a picture ““a black and white photo of a baseball glove.”, Norman would see “man is murdered by machine gun in broad daylight.”. When the standard AI would see “a person is holding an umbrella in the air.”, Norman would see “man is shot dead in front of his screaming wife.” -for more examples, feel free to visit their website in the bibliography.
Apart from the dark humor -and certain morbidity-, this experiment underlines the importance of the initial dataset used for learning. While most people are afraid of algorithms being biased and unfair, they should focus on understanding the learning process said algorithm undertook. Norman itself was a perfectly standard system; however it learned from the worst data set possible.
Probably within the next years, certainly within the next decades, we are bound to see machines making crucial decisions in our place. To ensure optimal decision-making, our first step is to make sure machines learn from the best we can offer.
After all, machines will only be psycho if we teach them to.

Bibliography:
Gallaugher (2016), Information Systems: A Manager’s Guide to Harnessing Technology, Gallaugher.
Norman-ai.mit.edu. (2018). Norman by MIT Media Lab. [online] Available at: http://norman-ai.mit.edu/#inkblot [Accessed 30 Sep. 2018].