It’s no secret that AI is at the forefront of any conversations in healthcare right now. But beware of following the shiny object.
With the focus on patient outcomes for monitoring healthcare results, it stands to reason why there is such a race to get to the marketplace with an AI-enabled device. According to CB Insights spending on AI for healthcare in 2018 hit a record high.
By June 3, 2019, the FDA is requesting that stakeholders in medical device clinical decisions provide them with feedback on how to regulate AI-based software. Due to the nature of the adaptive technology, and continually optimizing their functionality should AI devices be placed into a different regulatory framework?
There certainly many examples that demonstrate the benefits of using AI-enabled devices such as IDx which detects diabetic retinopathy, the primary cause of blindness. Viz.ai the developer of the decision support software that can analyze CT results.
According to Emerj.com, there are three primary applications that are gaining momentum in the AI space.
- Management of chronic diseases
- Medical imaging
- AI and Internet of Things (IoT)
One major challenge all companies are running into is the pool of talent that will help them build these AI solutions. At a recent event I attended in Chicago, the VP HR from Blue Cross Blue Shield of IL stated that when she posts a position for a Phd. Level Data Scientist, she may only get two resumes sent in compared to posting a Data Analyst position, she will get 100’s of resumes.
I don’t see any clear winners right now and the playing field has plenty of room for more players. It will be very interesting to watch how this will progress over time.