- Two Stanford University researchers argue the hype around artificial intelligence in healthcare has reached a “peak of inflated expectations,” FierceHealthcare reported.
- Writing in the New England Journal of Medicine, Dr. Jonathan Chen and Dr. Steven Asch, both from Stanford’s Department of Medicine, stated a greater appreciation of AI’s capabilities and limitations could prevent future disillusionment with machine learning technologies.
- They point to unfounded predictions that AI will supplant medical specialists, saying healthcare needs the strengths and capabilities of both humans and algorithms.
“Before we hold computerized systems (or humans) up against an idealized and unrealizable standard of perfection, let our benchmark be the real-world standards of care whereby doctors grossly misestimate the positive predictive value of screening tests for rare diagnoses, routinely overestimate patient life expectancy by a factor of three and deliver care of widely varied intensity in the last six months of life,” the researchers write.
AI-focused companies are already prepping technologies to make headway in healthcare. In February, the University of Pittsburgh Medical Center partnered with Microsoft to launch the first project under the new Healthcare Next initiative aimed at enhancing clinicians’ workflow. And late last year, IBM teamed up with NVIDIA to advance what the companies are billing as the “world’s fastest deep learning enterprise solution,” with implications for use in healthcare.
At HIMSS17, IBM President and CEO Ginny Rometty touted the promise of AI, but said five guidelines are required to realize its potential in healthcare. It must provide both a range of services and data transparency and be domain-specific, cloud-based and an open platform.
Still, artificial intelligence is a big leap from tools such as EHRs that most providers recently spent an arm and a leg on to help modernize their practice. The hype surrounding AI will likely present itself in the clinical space in more of a subdued way. Many thought leaders are expecting the tools to augment or help make practices more efficient. The industry is just at the edge of AI development and adoption so time will tell how the hype sounds five to 10 years out from now.