Despite current constraints, the clear productivity gains available from well-designed and targeted machine learning systems will drive innovation in medical device design.
From self-driving cars, to home assistants with voice recognition, it seems like every day there is some new application of artificial intelligence that is insinuating itself into our lives.
Although it is not as evident, medical device companies have been quietly implementing artificial intelligence for over 15 years, primarily in imaging systems. From products that enhance radiology images to assist clinicians’ reviews to those that highlight interesting regions of tissue or unusual cell populations in histopathology or hematology systems, medical devices using machine learning have been carefully marketed as improving the efficiency and quality of patient outcomes, while not straying into making diagnoses.
As consumer uses of machine learning grows, innovative medical device companies are introducing FDA-approved products using similar technologies as those employed by Facebook, Amazon and Google, and at the same time are moving from merely assistive systems to those that are making clinical suggestions. Some examples include: