Seeking a Revolution in Clinical Care Through AI

New computational techniques stemming out of the field of data science, including machine learning, computer vision, and artificial intelligence (AI) techniques (collectively, here—ML), stand poised to revolutionize clinical care. These include the decision making leading up to and surrounding clinical interventions, the decisions to use medical devices, and the ongoing therapies delivered via medical devices. While these techniques are powerful, becoming (or are, depending on industry) mainstream, and generally exciting, they are not without risk and can incur quite considerable time and capital costs to develop and deploy. In fact, as others have noted, the vast majority of measurables (cost, time, code, etc.) developed and incurred while developing any ML system are not the ML-algorithm itself.¹

There’s been a profound uptick in medical device companies making or wanting to make use of these ML techniques to meet unmet clinical needs and provide better therapies as these techniques may provide better and more adaptive insights than traditional methods. Meeting the need requires a focus on generalizing and standardizing t