Machine Learning Benefits in The Healthcare Industry

In the healthcare industry, there is an unspoken hierarchy of importance and relevance of information depending on where it comes from. One of the most reliable sources is the doctors’ brain. With machine learning, this can be made to be better or worse depending on how it’s applied. It’s no new fact that there Machine Learning Benefits in the healthcare industry, other industries, and beyond. In many industries, machine learning (also known as AI) has continued to see huge advancements both in its development and applications. These advancements have led to some concern for both ethical and practical concerns.

2018 will be the year that exposes where AI works and where it fails in healthcare. Applied to huge de-identified data sets, AI is already generating insights useful in population-based work such as accountable care and drug discovery. But AI fails badly when “resolving” to the individual care plan, mostly because the full set of data needed to treat a single human being is still too vast, complex, and mysterious for today’s computers and algorithms to handle and automate.

Machine Learning in the Manufacturing Industry

In The manufacturing industry, auto-workers fear that robotics would replace them and eliminate their jobs. Like in assembly lines, robots are being used at increasing rates and on more advanced tasks.

Machine Learning in the Service Industry

Also in the service industry, there have been talks of robots that can make coffee almost as well as a human and are already being used to replace baristas in Japan. The robots can make different drink orders from mochas to cappuccino and espresso shots. If anything right now, they serve as a gimmick as cafe-goers are fascinated by them and keep taking pictures. Robots could also be used to answer customer care phone calls in more advanced ways.

Machine Learning in the Workplace

General purpose AI is still decades away. However, narrow AI applications will make a big splash in enterprise support functions in 2018, as call centers, finance and IT executives begin to move conversational AI, image recognition and autonomic applications from pilot mode into production. These applications will complement existing robotic process automation implementations, turbo-charging employee productivity and operational speed to levels far beyond traditional industry benchmarks—Stanton Jones, Director and Principal Analyst, ISG

Machine Learning and Self-driving Cars

This is one of the more popular AI projects being developed in recent times. Different companies like Google, Apple, and Tesla have been trying their hands on making self-driving cars. However, despite recent breakthrough, self-driving cars have been experimented with (even successfully) as far back as 20 years ago. In 1995, Mercedes-Benz managed to drive a modified S-Class mostly autonomously all the way from Munich to Copenhagen. According to AutoEvolution, the 1043 mile journey was made by stuffing effectively a supercomputer into the boot – the car contained 60 transputer chips, which at the time were state of the art when it came to parallel computing, meaning that it could process a lot of driving data quickly – a crucial part of making self-driving cars sufficiently responsive. Apparently, the car reached speeds of up to 115 mph, and was actually fairly similar to autonomous cars of today, as it was able to overtake and read road signs. As Tesla announced in 2016:


“All Tesla Cars Being Produced Now Have Full Self-Driving Hardware. … We are excited to announce that, as of today, all Tesla vehicles produced in our factory – including Model 3 – will have the hardware needed for full self-driving capability at a safety level substantially greater than that of a human driver.”

Machine Learning in Sales

AI will accelerate the extinction of simple order-taking sales in the sales and customer service industry. It will enhance consultative sellers’ ability to win more customers by effectively articulating business value and being able to access a plethora of data at lightning speed in order to meet a customer’s request.  Also, AI can complement sales reps as opposed to replacing them. AI-powered sales learning tools will suggest actions, micro-training, and just-in-time content for reps—based on an assessment of the customer’s needs, the rep’s skills and experience, and the competitive dynamic during sales, like the way Netflix recommends movies.

Machine Learning in Entrepreneurship

As new businesses continue to spring up and existing businesses continue to grow, they are also making a move towards intelligent enterprises. As companies start to grow out of proof of concepts they will seek to apply AI to their business and business model at an increasing rate. Thanks to mature machine learning algorithms, disruptive business models will emerge. They will force whole industries to realize that digital transformation is not just trending, but essential to remain competitive. Meanwhile, deep learning is established as the standard machine learning commodity, but will now strive for more efficiency and scalability within the systems. Finally, we can await further breakthroughs in reinforcement learning and will see academia further adjust to industrial research to ensure their competitiveness.

Machine Learning and Data

As advancements continue to be made, one thing that’s constant is that for any AI or Machine Learning system to function properly, it needs data. As more data is available, it means there will be more information to be processed and used to the advantage of the users of the system. As more data is available, we have better information to provide patients. Predictive algorithms and machine learning can give us a better predictive model of mortality that doctors can use to educate patients. As it grows, there will be a possibility that smaller companies or entities will merge their existing data pools with larger systems to have one bigger and more comprehensive system. At some point, we may see regional data hubs with datasets customized for geographical, environmental, and socioeconomic factors, that give healthcare systems of all sizes access to more data. For the healthcare industry, as larger datasets begin to run machine learning, care can be improved in more specific ways.