In short, the answer is yes. Recent research by computer scientists Kris Hauser and Casey Bennett at Indiana University has shown that computer-based decision making can potentially save enormous amounts of money for health-care consumers when compared to typical human decisions.
Basically, the researchers trained an AI that works through two distinct processes to learn how to make effective clinical decisions from real-life information (obtained from electronic medical records). They then compared the AI’s decisions to decisions made by human physicians. Turns out, not only did the AI outperform the human docs, it freaking smoked them:
That’s pretty impressive savings. Perhaps the most interesting aspect of this work, though, is the ability of this AI to perform these tasks even with missing data. This seems to be due to not just a reliance on one method of data processing, but the combination of the two used. As the researchers write:
Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments.
The results are not very surprising to me, as numerous studies in my field (clinical psychology) have long shown that using computer-based or algorithmic decision making can greatly improve diagnosis or even predict who will not complete therapy.
A reliance on algorithms in combination with clinical experience and accumen could prove highly beneficial to patients. Maybe clinical diagnoses would be more accurate than the 50% rate found in one large study, or a larger portion of healthcare providers would follow best-practice guidelines. It could also address a major plaguing health-care systems worldwide – cost increases with no significant gain in quality.
Here’s hoping that this is another step towards building a real-life medical tricorder, able to make accurate medical decisions with the wave of a hand!