How will AI impact Healthcare?

What will the future of healthcare look like?

How will AI impact Healthcare?

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Imagine taking a pill no one else can take, guaranteeing good health only for you. That’s the future of AI in healthcare, far beyond ChatGPT. AI already analyzes medical scans of your body with an accuracy that often outperforms professionals. During my Ph.D., I’ve seen firsthand AI in healthcare transforming radiologists’ work, making diagnosing conditions like multiple sclerosis easier and more precise. And this is just the tip of the iceberg regarding AI’s potential in healthcare.

So what will be AI’s impact on healthcare? It will be a game-changer, not just for radiologists but for everyone. It will make healthcare more accessible and cheaper, enhance diagnostic precision, personalize patient care, streamline administrative processes, save professionals’ time, save lives and much more.

Ok, that sounds too good to be true. And it’s all in the future. What’s implemented right now in medicine that professionals, or even us, potential patients, can use based on AI technologies?

Before we dive in, I want to say that I am not a medical expert or healthcare professional. I was a Ph.D. student in biomedical engineering and now switched to full-time educator and popularizer in AI. My goal is to make AI more accessible through videos and other educational resources. During my research for this article, I had the pleasure and opportunity to chat with a few medical AI experts, including Mona Flores, head of Medical AI at NVIDIA and previous chief medical officer and certified cardiac surgeon, who gave me amazing real-world examples of how AI is currently being used, from the nearby hospital up to your wrist if you’re using a smartwatch! Before we dive into this episode focusing on healthcare, I just wanted to mention that I will be attending the NVIDIA GTC event later this March, where professionals like Mona talk about AI’s application in various industries, including healthcare. Check out her talk on How Artificial Intelligence is Powering the Future of Biomedicine! All the events are completely free remotely. You can attend and check all the sessions online.

Here’s how AI impacts the medical field, but first, don’t forget to subscribe to the channel and my newsletter to stay ahead of these exciting developments and understand them.

Current use cases of AI in Healthcare

AI is already transforming healthcare, from research like my Ph.D. work for automating brain lesion detection to companies like Siemens Medical and GI Genius revolutionizing tumour identification and improving colonoscopies. Even your Apple Watch is part of this wave, monitoring heart rhythms 24/7 to keep you safe.

A cool real-life use case of AI Mona shared with me is a digital stethoscope from Eko Health that can listen for you and spot-check for irregular murmurs for doctors instead of having to learn and understand all the different sounds. Yes. This already exists.

Mona also shared another application that is even closer to us. In fact, it may already be on your wrist right now. The Apple Watch tracks your heart health and will notify you if something is going on, like an irregular heart rhythm, that could save you by preventing potential strokes. The moral of the story: get an Apple watch or other intelligent watch, just in case, before it’s too late. It may be a good investment!

But you most certainly already knew about that, and, fortunately, that’s not all AI can do. There are tons of technologies already helping professionals run MRIs at a lower price and improve the patient’s experience by reducing the time it takes to run scans.

And it’s not only beneficial to experts in hospitals. We can now say, “there’s an app for that,” even for health issues. Thanks to the first-ever FDA-approved AI device, we can detect early signs of diabetic retinopathy, a complication that affects your eyes caused by diabetes, through just a picture of your eye, helping you prevent damaging your retina without waiting months, if not years, to see an opthalmologist.

AI can also help directly with drugs. For instance, DeepMind’s AlphaFold makes drug discovery faster than you can find your keys late at night. Ok, maybe not that fast, but it does it in a similar manner. While you touch and feel your keys one by one to find the right one in the dark, AlphaFold figures out the complex 3D shapes of proteins for creating new medicines. If a protein is known to contribute to a disease, scientists can use AlphaFold to understand its shape and then design a molecule (a potential drug) that can attach to the protein, inhibit its function, and thus treat the disease. This process significantly speeds up the early stages of drug development, making it quicker and cheaper to find starting points for new medicines.

Likewise, AI can help with the dosages of these medicines, catching mistakes before they happen. It can do the same with food, keeping you on a good diet. Probably an overkill method versus skipping the drink and dessert this time.

Sometimes medicine isn’t enough even if you take care of your body, and you end up relying on surgery to remove or fix parts of yourself. A cool use case is one I was directly involved with in an internship I did at CAE Healthcare. I worked on robotic-assisted surgeries with incredible precision, either to help surgeons practice or allow for remote surgeries. It’s as if the surgeon has an extra pair of small perfect hands, super steady and accurate, with a second brain that can correct the small imprecision moves automatically, making operations safer and helping patients heal quicker.

And finally, of course, there’s ChatGPT, useful even in healthcare. We can use those text-based models to handle scheduling, transcribing what’s being said in appointments, and even help write and plan prescriptions automatically as doctors say it in the appointment. Imagine a tool that does all the boring stuff for doctors so they can focus more on you.

But there’s one problem, or limitation, with all these super promising AI-based applications. It requires data. Lots of data. From annotated brain images to patients’ information. The healthcare industry is composed of almost only sensitive and protected data. So you cannot really scrape the whole internet and build a good model like ChatGPT. ChatGPT is not good for medical diagnosis; it’s just like Google. If you use it right now for an irregular cough you had, you’ll be convinced to have cancer in a few minutes. Fortunately, there are approaches to counter that. I worked on one of the available solutions for training AI models with such sensitive data in my Ph.D., called federated learning.

This approach allows AI models to learn from multiple locations without taking the data out of the hospitals. It achieves that by training individual models in each location in a step by step fashion where, at each step, we combine these models into one more general and better performing. This way, we can develop powerful AI systems that detect brain lesions or other diseases from MRI scans. This both preserves patient confidentiality and enhances the AI’s diagnostic accuracy providing more data than a single hospital could ever produce, sharing the AIs’ knowledge but not the data to acquire this knowledge. This approach is really interesting for current and future applications of AI in a field like this one.

Potential future use cases of AI

While these current applications are all super exciting, the future looks even more promising. Think of today’s AI applications as your first ever Blackberry with internet access. I remember the feeling of being one of the first in my class to have it. It was so cool. Now compare the 2007 Blackberry with the iPhone 15. In fact, there’s little we can compare. So much has changed. That’s what will happen with every existing solution we saw.

The Apple Watch checking your heart is just one of the first accessible monitoring applications we have access to. There will be many opportunities for remote-health technologies as AI and our mobile devices improve.

The first game-changer application will be virtual health assistants. They will be even more on your back than an annoying acquaintance who doesn’t understand your “no” signals to go out with them. It will check you 24/7, offering constant health monitoring and personalized advice tailored to your unique genetic makeup and lifestyle, suggesting the ideal amount of cups of coffee instead of suggesting uninteresting activities to do with them. This leap forward will shift the focus from hospital-based care to home-based care.

Personalized medicine will make all of healthcare similar to visiting an optometrist, where just as you receive glasses tailored specifically to your vision, AI will leverage genomics and real-time data to create treatment plans and drugs designed for an individual’s unique condition. Going away from the one-size-fits-all approach, moving toward unique patient-specific treatments to the ultimate pill I mentioned at the beginning of the article.

This transformation will make healthcare more affordable and accessible. Diagnostic tools and knowledge will be at everyone’s fingertips, just like the eye picture example we saw, enabling early detection and intervention like never before directly from your home — no more 6 months of waiting to see a professional, and once you are finally in, hours of wait in the waiting room.

Just as electricity and the internet became indispensable, AI’s adoption will become necessary for healthcare companies to stay competitive.

However, this future is not without its challenges. Biases in AI and data privacy concerns are issues that will most certainly slow down progress. Healthcare has much graver consequences than generating unfair text with ChatGPT. Not that it’s not important, but small ChatGPT mistakes might not kill as many people as a small pill dosage mistake might. We need to fix the biases before deploying the models. We can’t test in the wild like OpenAI does and fix when something happens. It may cost lives.

Still, it’s important to work on implementing it in hospitals as it will change professionals’ work. AI will enable them to spend more time on direct patient care, improving job and patient satisfaction and, even more importantly, reducing overworked professionals and burnout. So I guess it certainly isn’t all bad, even if biased and imperfect. The future of AI in healthcare is not just about technological innovation or replacing professionals; it’s about transforming the field to allow for better, more efficient, and personalized healthcare for everyone. Medical advice, consultations, and even diagnoses will soon change forever, especially in remote or underserved areas.


In conclusion, the transformation of healthcare by AI has already started, and it will only grow from here. Remember that even though ChatGPT and the current companies I mentioned are impressive, this is just the start, and we see the worst results we’ll ever see as it will only keep improving. Do you still think this is a ‘terminator’-like future?

I encourage everyone to explore how AI can revolutionize their own fields, which I intend to cover in my future articles of this series, so stay tuned for that. I also have a newsletter in case emails are easier for you to track than YouTube channels. By the way, what field would you like me to cover next? It might be the next one I tackle! Thank you for reading the whole article, and I will see you in the next one!