AI adoption for real-world healthcare settings can’t happen in a vacuum. 

ChatGPT is only so-so at letting physicians know if any given clinical study is relevant to their patient rosters and, as such, deserving of a full, time-consuming read. On the other hand ... 

Imperfect algorithms. Resistant clinicians. Wary patients. Divisive disparities. The plot ingredients of a flashy techno-thriller coming to a cineplex near you? No—just a few of the many worries that provider organizations take on when they move to adopt AI at scale. 

“ALPHV/BlackCat and their affiliates haven’t attacked Change Healthcare. This time, they’ve attacked our families.”

It’s more critical for U.S. healthcare to get medical AI right than to get it adopted far, wide and ASAP.

Scientists are people too. As such, when engaged in research projects using AI, they must resist the very human impulse to over-delegate tasks to algorithms.

Years will pass before the global economy’s healthcare sector sufficiently leverages AI to build major financial muscle off of it. And even then, industry players are likely to see gains well ahead of hospitals and health systems. 

Pricing transparency report cards for 2024 are out, and the grades for U.S. hospitals have dipped for the first time since the federal government reset the standards in 2021.