With so much industry buzz around artificial intelligence’s ability to transform healthcare, it’s easy to lose sight of the fact that not all AI is created equal.To appreciate AI’s full potential to improve healthcare quality, lower costs and enhance patient access, it’s important to understand the difference between autonomous and assistive AI.
Assistive AI can take measurements and help physicians make decisions, working like a second set of eyes to review medical images, for example. In contrast, Autonomous AI solutions are capable of actually making clinical and treatment decisions that impact patient care and outcomes.
To illustrate the difference, I like to point to the self-driving car industry, which is working to navigate the transition from assistive to autonomous AI. Today, there are cars on the market that can parallel park, drive on the highway, and avoid road blocks – but these vehicles still require a human to sit in the driver’s seat and provide oversight of the system. That’s assistive AI.
The industry is working toward a different use case though – one that functions without any human intervention or oversight whatsoever. In that context, a truly self-driving automobile takes on a lot of responsibility; it has to be capable of responding to a host of different scenarios and challenges – weather, road signs, pedestrians – without human intervention. That’s autonomous AI.
What autonomous AI means for healthcare
From an algorithmic performance perspective, the performance bar is much higher for fully autonomous vehicles versus assistive ones. When cars take over driving responsibility there is no human “safety net” to fall back on.
As it relates to healthcare, the result is that autonomous AI must undergo far more stringent testing before it can be used in the practice of medicine, which enables clinicians to have confidence in the clinical decisions autonomous systems make.