Agnieszka Kloc

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With each day some new developments in data science and machine learning are being published. The researchers constantly invent new ways to improve the performance of their predictive algorithms, thus improving the accuracy of the computers’ predictions on any given subject. One of such subjects, which should be of particular interest to us – humans – is medicine.

With the constant increase in the computation power and exponentially growing amounts of data, the researchers could potentially save a lot of lives with the use of advanced data science solutions. For example, research presented in the journal of the American Academy of Neurology states that the potential for artificial intelligence in precision medicine is significant. IBM Watson, a question answering computer system, provided a report of actionable insights within 10 minutes, in comparison to 160 hours of human analysis normally necessary to reach analogical conclusions (Monegain, 2017). This can often be of crucial difference when dealing with illnesses such as malign cancers, as some of them have a median survival of less than a few months following diagnosis.

Aside from genome analysis, there are countless areas in medicine where machine learning and artificial intelligence solutions can mean a difference between life and death (or very serious health complications). Currently, researchers around the world are trying to incorporate data science applications in detection of diseases such as autism, Parkinson’s, Alzheimer’s to name just a few! All of these sicknesses have one thing in common: early detection always improves the chances for a recovery or prevents further complications. And in this very sense, the AI can help people. Not only it is able to recognize the symptoms of a disease faster that a human can, but also it is able to do so at a larger scale, providing necessary diagnosis to the people in need. For instance, Google is launching an experiment to use machine learning to discover a diabetes-related eye disease in India, where the number of people with diabetes is around 70 million (with approximately 400 million people worldwide) and not a large percentage of them would normally receive a proper diagnosis in time (Simonite, 2017).

Another important factor, also connected to the accessibility, is the fact that a lot of people must postpone visits to the doctors because they cannot afford a private visit, while the wait time for a public one can even be around a year. And with the use of computers the time needed for an accurate diagnosis, as well as the number of specialists to be seen before obtaining it, is drastically reduced (Molteni, 2017).

Of course there are disadvantages of using AI in medicine. Firstly, there is a rising fear among the doctors that they will lose their jobs and be replaced by machines. According to an article in the New England Journal of Medicine, radiology and pathology are primarily susceptible to the power of AI, due to the fact that these jobs are based on pattern matching and machines can perform such tasks with surprising accuracy and speed (Asay, 2017). However, AI should mostly complement the work of doctors, enable them to perform their job more efficiently, thus help more people. Secondly, there are many issues regarding the data. Some people are not always aware that by signing a particular form they enable a company (or companies) to use their private data for research purpose (Molteni, 2017). What is more, for the algorithms to have a high accuracy, they need to have a lot of data at their disposal, which is not always feasible. For example, in the process of detecting the case of autism among kids, a lot of data needs to be gathered from MRI scans. This is not only time-consuming, but also expensive procedure. And it would not be realistic to have every child scanned in order to have a sufficient dataset (Vlasits, 2017).

Summing up, the rapid development of artificial intelligence and big data promises a future in which computers will be able to assist the doctors in providing quick and accurate diagnosis, thus saving human lives. However, one cannot forget that the algorithms should assist the doctors, instead of taking over their jobs. There will always be a fear that a machine provides an inaccurate prognosis, which can either scare the patient or make him calm, when he or she can be seriously sick. That is why human input is always valuable, to evaluate the machine’s diagnosis and verify its correctness. In other words, we should be very optimistic about the possibilities offered by artificial intelligence, but at the same time should not expect a bunch of robots treating patients in the hospital anytime soon.

References:

Asay M. (2017, January). Why AI is about to make some of the highest-paid doctors obsolete. Tech Republic, retrieved from: http://www.techrepublic.com/article/why-ai-is-about-to-make-some-of-the-highest-paid-doctors-obsolete/

Molteni, M. (2017, August). Want a diagnosis tomorrow, not next year? Turn to AI. Wired, retrieved from: https://www-wired-com.eur.idm.oclc.org/story/ai-that-will-crowdsource-your-next-diagnosis/

Molteni, M. (2017, September). 23andme is digging through your data for a Parkinson’s cure. Wired, retrieved from: https://www-wired-com.eur.idm.oclc.org/story/23andme-is-digging-through-your-data-for-a-parkinsons-cure/

Monegain, B. (2017, July). AI can speed up precision medicine, New York Genome Center-IBM Watson study shows. Healthcareitnews, retrieved from: http://www.healthcareitnews.com/news/ai-can-speed-precision-medicine-new-york-genome-center-ibm-watson-study-shows

Simonite, T. (2017, June). Google’s AI Eye Doctor Gets Ready to Go to Work in India. Wired, retrieved from: https://www.wired.com/2017/06/googles-ai-eye-doctor-gets-ready-go-work-india/

Vlasits, A. (2017, June). AI could target autism before it even emerges – but it’s no cure-all. Wired, retrieved from: https://www-wired-com.eur.idm.oclc.org/story/ai-could-target-autism-before-it-even-emerges-but-its-no-cure-all/

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Can you imagine what your life would be like if you could avoid being stuck in traffic jams every time you go to work by car? Have you ever heard about a flying car and said to yourself – “no, it is too futuristic, maybe someday in the 4th millennium…”? If yes, then you can actually be quite surprised, because experts claim that we are very close to have our lives completely transformed by this new technology.

Many companies, such as Airbus, Uber, Google, Toyota and even a Dutch firm, Pal V, are investing big money to put this idea, which seemed unrealistic at best not a long time ago, into life. One of the players on the market is Aeromobil. Their prototype is a petrol-fueled machine that can work both in the air and on the roads. The car currently costs €1.2m and is ready to take two people on its board. The main drawback of this particular model is that it requires an airstrip to take off (Rudgard, 2017).

It might be a good question to ask ourselves, why do we sincerely need flying cars? Is it only our technical “whim”? Well, the answer was somehow already provided at the beginning of the article. Traffic congestion grows unrelentingly and our civilization tried many solutions to overcome it: expanding roads, building underground public communication systems or ride-sharing. But they still seem as not sufficient to tackle the problem. That is also why Aeromobil’s vehicle is so impractical, even if it can fly, it still requires too much space to take off and land. A flying car should be capable of VTOL – Vertical Take Off and Landing – to be convenient to use by inhabitants of the most congested areas.

Another problem with flying cars lies in the process of flying itself. Earning a pilot’s license might be too much of a bother for an average consumer, so the engineers from Airbus plan on building a self-flying machine. They already have their prototype which is going to use laser scans in order to perform the landing process (Stewart, 2017). Given the cost of the car itself, it is more plausible that the cars will be owned by the government or big taxi corporations and shared, rather than owned by individuals.

Surely, with better technical advancements, the flying cars will probably get less expensive and maybe even affordable for an average person, but some researchers worry that a consequence of it might be actually increasing congestion instead of reducing it. As an example to illustrate the reasoning can be used the fact that we are exchanging more messages now, compared to when we had only paper letters and landlines (Stone, 2017).

Additionally, what cannot be overlooked is the issue of monitoring air traffic and reducing the risk of accidents, especially for vehicles with no human interaction. The airspace itself is getting more and more “crowded” with all the planes, drones and now, possibly, flying cars too. Even more complex Air Traffic Management and Control Systems must be constructed and implemented so that safety of all parties is ensured.

Will flying cars become a great technological breakthrough of the XXIst century? Or will they rather remain a futuristic fantasy?

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References:

Rudgard, O. (2017, September). Coming to a road near you: AeroMobil’s flying car preparing for take-off. The Telegraph, Retrieved from: http://www.telegraph.co.uk/news/2017/09/17/flying-car-company-aeromobil-says-radical-concept-vehicle-will/
Stewart, J. (2017, August). Airbus uses lasers to teach its flying car to land. Wired, Retrieved from: https://www-wired-com.eur.idm.oclc.org/story/airbus-vahana-flying-car-landings/
Stone, M. (2017, June). Flying Cars Could Happen. But They’ll Probably Create More Problems Than They Solve. Greentech Media, Retrieved from: https://www.greentechmedia.com/articles/read/flying-cars-might-happen-but-they-might-create-more-problems

Can machines replace doctors anytime soon?

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The future is close – flying cars (almost) ready to take off

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