Reported numbers from Accenture are talking about $6.6B in market value by 2021. $6B / year is not a big market but the compound annual growth is explosive with a 40% CAG and a projected market value of $150B by 2026.

If we take a step back and think about these numbers we are talking about the biggest missing opportunity of our lives as healthcare participants if we do not enter the Artificial Intelligence train in healthcare now. Since it is very difficult for us to completely grasp the power of exponentials let us visualize it with a graph.

While most of the growth will be eventually consolidated to a few major players, the arena is still open. It is not by accident that AI has been compared with the equivalent of the new electricity. Imagine owning the electricity of a $3T / year US healthcare market. Now is the time to act. But how can we avoid the pitfalls?

The problem is that for any ambitious manager, we cannot afford to stay out of this potential growth. Opportunities like this have rarely appeared historically. But with every significant growth there is the potential for significant risk. The Billion Dollar Question then is:


Please allow me to shortly introduce myself: My name is Alexandros Louizos. I am trained in vascular surgery in UK and in 2015 I left my career as a surgeon to join the AI revolution in medicine. I have created multiple startups in the space and have talked with many healthcare players. I have lived the hype and fall of big companies’ efforts to enter the space. Allow me to share my experience about the future of AI in healthcare. It might help you get inspired and create value by entering the space successfully but with less risk.

INSIGHT  1: Avoid Super Futuristic, Replace and/or Assist the Doctor Scenarios

I know that you might be finding that this is the most exciting opportunity. It is mesmerizing to have a machine diagnose at the same level as a doctor. It is also the riskiest path. The one with the highest regulation. The one with the highest resistance.

Doctors do not need to be replaced. They do not want to be assisted also in very risky and high responsibility scenarios. What is left actually for grabs is scenarios like “preliminary diagnosis” or “assistive dosage correction” etc, which are not exciting and still very difficult to integrate with the electronic health records. Moreover, access to the data to train these systems is next to impossible (think like 12 months hustle to access some data, if you are lucky, while the risk of accessing this data is huge and the “guru” responsible for giving you access is not available and many other limitations that will take you 18 months to eventually get a pilot complete without lots of potential for change of path and pivot in between).

NSIGHT 2: Follow The Flow Of Money In Healthcare And Solve Problems Along That Path

Whether we like it or not the healthcare system was not built for preventative care (I built a startup in this space, connect with me if you want further insights). We are talking about reactive vs proactive medicine but reactive medicine is well aligned with human psychology. Very few people would optimise for their health early and in a preventative manner especially if it takes time and money for them to do so. Out of my personal research the people that would actually do preventative medicine is less than 3% of the total population and would want to spend less than $800 per year with a sweet spot of huge value but at a price point of $500 / year which makes the whole effort unsustainable.

On the other side look into the numbers of fraud in the healthcare industry. What can you do to help prevent fraud? Both on the insurance side and the healthcare side. We are talking about $150B / year potential savings. Or how about optimizing the healthcare path of patients already in the system? Or how about creating administrative assistance or nursing assistance? Optimization of the workforce for healthcare (shifts, training, wellness for doctors). These are very important issues that are valuable and totally overlooked for more flashy and catchy elements of AI healthcare. Use AI always. But target the lucrative pathways, not the hype-y flashy ones.

INSIGHT 3: Genomic Data Are Still Lagging In Clinical Value While Patients Need More Crucial Parts Solved For Their Healthcare Experience To Become Better.

I have had my full genome sequenced. The value of it was marginal. The hassle was major. Had to wait for 6 months for the whole thing to be completed. The data were not presented in a very concise way. I am a doctor and still struggled to understand some insights and risks. There is a very human-driven fear factor involved with knowing, while you can only do as much to decrease the risks that you learned about e.g. eat better, sleep better. I will write a whole article about my experience in the future but for the moment I would say that genome-driven preventative medicine did not make a visceral sense from a consumer point of view.

On the other side, there are so many unresolved patient experience problems that artificial intelligence could help with. For example, the huge discrepancy of prices for medical care among different organizations. Why not solve this problem for the consumer? The problem of patient adherence to drug prescriptions? The problem of efficient clinical trials? How about the presentation of good patient-friendly information on the internet that does not involve cancer mentioned for each headache symptom searched? So many problems, very few solutions.

As a conclusion I would say that to be successful in the healthcare artificial intelligence space, we need to focus on the right solutions that make visceral sense from a healthcare consumer point of view, while the savings and potential for growth can be significant.

What do you think about these insights? Do you want to continue the discussion? I would be very happy to discuss your ideas. Connect with me or comment below. Can’t wait to hear more about your experiences, your insights, and your ideas.

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Alexandros Louizos, MD
Alexandros Louizos, MD

Alexandros Louizos, MD is a vascular surgeon that left his career in surgery in 2013 to join the artificial intelligence revolution. After working for 2 years as a data scientist he decided to leave the corporate career to start his own company in 2015. He is a 2x entrepreneur of AI-related companies, Galaxy.AI (VC funded with $2.9M), and his latest venture is bootstrapped. He has designed and executed artificial intelligence systems currently in production is Fortune 500 companies. What gives him happiness is helping other dreamers to learn data science.

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