AI in MedTech: Promising Technologies and Applications

If you are in the MedTech industry you are very familiar with the concept of AI. As we know AI is now more than a concept and it seems that just about everything has the "AI" component attached to it. As we congratulate our client Ortoma AB on their continuous revolution with their 2D to 3D solution that will be present at ISTA in New York this fall, it is a good time to take a closer look at what AI is at this point in time.

Let's start with a review of terminologies AI vs Machine learning: Are these two terminologies interchangeable? From our perspective, not really. Machine learning is the necessary step to getting any technology to claiming to be true AI. It is, in a sense, the necessary training to get any device to be able to exercise its full potential. No product to date, has come out of the gate without training or having intelligence loaded. It is based on learnings from a wide range of patient, clinical, geo-economic, demographic and scientific data. With Machine Learning comes effective AI.

Take a look at some of the most promising technologies with AI on the market today:

Diagnostic
The largest portion of AI diagnostic solutions seems to be currently in the cancer diagnostic segment. Cancer Diagnosis is done is many ways, but the lion share of early detection lies in imaging. AI algorithms can analyze medical images (e.g., X-rays, MRIs, ultrasounds, CT scans, and DXAs). AI lands itself very well in supporting a field where early diagnosis is key in saving lives especially as the number of pathologists and radiologists needed is so far below the demand. As such, we see these AI solutions as key to cancer diagnosis progress and the care of patients worldwide. With that said, unbiased AI solutions are still limited in number to make these diagnostics truly be effective regardless of demographics.

Clinical Tools
From Surgical tools (such as in orthopedics) with navigation, planning, and patient related instruments and robotics, to colonoscopy and endoscopy systems, to managing devices in diabetes or organ support therapies, (just to name a few), AI is already well understood as a critical partner to providing care. The race here is towards continuous improvement and constant innovation to provide clinical support faster and better, and at a lower cost of care.

Disease Management
From following the progression of a disease, to forecasting the outcome, to assisting in choosing a particular therapy, to providing ongoing support such as in mental health through apps, the applications of AI in disease management have been quite varied and are in very different states of being. Some are now subject to FDA regulations while other are not. For example, we are starting to see more and more patient disease management platforms to help patients navigate their own therapies. It is still unclear, however, how these apps will be managed in the future

Clinical Trials and Treatment
This is where MedTech and Pharma come together the most when it comes to AI. From diagnostic (as mentioned above) to determining which drug a patient should take or what clinical trial would be best, AI is helping to map the path for better care. AI is breaking grounds in clinical research for example, as there is a company currently using AI to create a twin from a real patient to compare different treatments paths and their effectiveness. This technology is still in its infancy but it does makes you see that the need and possibilities for AI solutions is truly only in its infancy.

Improving the Healthcare System
Whether its the management of medical records, improving access to healthcare by being better able to forecast the need for staffing, reducing the redundancies of tasks, or understanding the current and future clinical trends and needs, AI is already making a lot of progress and sizeable changes. Add to the list security and fraud detection and it is easy to see that AI has already showcased its benefits and it will only continue to do so. We will note that these new solutions are often not as regulated as devices and therefore are able to go to market faster.

Digital health and AI are now closely intertwined and we seeing how the path of AI will continue to support and drive better care in the future.