MTI Start-Ups See Beyond the Data

In every major technological field today, artificial intelligence (AI) is more ubiquitous than ever before. No longer confined to software engineers and roboticists, large language models like ChatGPT have turned AI into a household product that laypeople can use to learn and create, saving time and effort in the process. In the medical industry, AI is perhaps most advanced in the imaging sector. According to various estimates, among AI/machine learning medical device approvals, radiology applications account for 70-80%, far more than cardiology and neurology.

Medical images, often the first line of a diagnostic workup, have always been a rich source of information and patterns, much of which is not visible to the human eye. Here, AI lends additional expertise to facilitate screening for early detection and diagnosis, and not just that. In many cases, by streaming workflows to save time and create consistency for both image acquisition and interpretation, AI can increase radiology capacity in ways that both increase patient access and hospitals’ bottom lines. But the same arguments apply to surgical or other specialties to which AI is applied, where it brings time savings, predictability, accuracy, and a leveling of expertise that increases capacity.

As the medical industry becomes increasingly overloaded with patient demand, a burden that AI tools may help to relieve by streamlining workflows and fast-tracking diagnostics and care, logistical and regulatory barriers have made adoption a more deliberate process. Looking to improve clinical outcomes in different areas of medicine are three MedTech Innovator cohort companies, RIVANNA, IMVARIA, and Anumana, whose AI solutions are designed to increase practices’ accuracy and efficiency. Rivanna aims to improve the placement of epidural needles for spinal anesthesia, while IMVARIA and Anumana are focused on extrapolating diagnostic insights from the most accessible imaging formats.