Future employment in the healthcare and medicine industry


Disruptive technologies will transform the healthcare job market. Although some tasks and positions will become obsolete, new medical professions will gain ground. Organ designers, robot companion technicians and telesurgeons in the first part of my article series.

Profession re-design: in progress

As in the era of the various Industrial Revolutions, many people are currently afraid that robots and artificial intelligence will take their jobs. It is without doubt that disruptive technologies are changing healthcare, medicine and pharma, as well as the way we gather medical information or how we interact with medical professionals and caregivers. It is true that robotics, AI, genomics and the wearable sensor industry will remove existing jobs. But I cannot stress enough that it will also add new ones – as it happened in previous historical eras with other professions!

Just as the automobile took over the place of the horse carriage in the early decades of the 20th century. The sons of horse carriage drivers, who saw the huge possibility in the appearance of cars and other motor-driven vehicles, decided not to go against technology and started to use cars to build up their business. If you are curious about how Hollywood documented the process in its own way, watch the cute 1928 silent movie, Speedy.

Potential new jobs in healthcare

As I am certain that the huge waves of technological change transform the medical professional palette, based on the current and prospective trends in digital health technologies I envisioned what potential new professions could appear in our lives.

If you have an idea about another new job of the future, please let me know and I will keep on improving the list.

1) What to do when your robot companion does not respond to your queries? Call a robot companion technician!

Somewhere in the 2030s: Lucy decided to buy a robot companion for his dad who lives alone in a big house in the suburbs of Nashville to help with the household, to make sure that he takes his medication or calls an ambulance whenever it is necessary or just to keep him company. The robot, Ted looks almost like a 60-year-old man with wrinkles on his face and grey hair. He was designed by a robot companion engineer, who contacted the family to let them describe what kind of robot they want and turned their wishes into reality. He could have shaped the robot as a dog or a cat as well.

How medical device CEOs can navigate digital health disruption

In the first of a series of three articles, we get global leaders, McKinsey & Company's insight on the medtech market right now. They give their expert advice to medical device companies, explaining how they can navigate through digital disruption.


When the robot arrived at their doorsteps, the family followed the instructions to put Ted in operation. Although the process took a while, it was not that difficult as assembling an IKEA furniture, still, something went wrong. So they called the company, who sent a robot companion technician specialized in this particular type of robot companion. The technician fixed a small electric failure, and told them to call him anytime they experience a problem. He reassured them that there are only a couple of robot companions assigned to him, which he knows thoroughly, and he will be available 24/7.

2) Want to teach algorithms? Become a deep learning expert!

Narrow AI is already reality: companies are already looking for highly skilled data scientists and deep learning experts in every field. AI-based algorithms will not only assist medical decisions, but also dominate how healthcare is organized and how health insurance is determined. We will need experts who can help algorithms learn about a certain topic by themselves making them smarter with every use.

How is it done? Deep learning algorithms are basically artificial neural network inspired by the human understanding of how our brain works. With gazillion of different interconnections between the neurons. But, these artificial neural networks have discrete layers, connections, and directions of data propagation. When it has to come up with a decision, – e.g. about whether an MRI image shows a tumor, data runs through various layers of neurons. Before it could get things right, these artificial neural networks need lots of training. It needs to see hundreds of thousands, even millions of images so that experts can tune the weightings of the neuron inputs so precisely that it gets the answer right practically every time – no matter whether it is a human face on Facebook or a malignant tissue.

Images pulled from original article.

This article first appeared on Medical Futurist and was written by Bertalan Mesko. You can read the full article here.

About Medical Futurist

Dr. Bertalan Mesko, PhD is the Medical Futurist. A geek physician with a PhD in genomics and Amazon Top 100 author, he envisions the impact of digital health technologies on the future of healthcare, and helps patients, doctors, government regulators and companies make it a reality.

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