Imagine walking into the Library of Congress, with its millions of books, and having the goal of reading them all. Impossible, right? Even if you could read every word of every work, you wouldn't be able to retain or comprehend everything — even if you spent a lifetime trying.
Now let's say you somehow had a super-powered brain capable of reading and understanding all that information. You would still have a problem: You wouldn't know what wasn't covered in those books — what questions they'd failed to answer, whose experiences they'd left out.
Similarly, today's clinicians have a staggering amount of data to sift through. Pubmed alone contains more than 34 million citations. And that's just the peer-reviewed stuff. Millions more data sets explore how factors like bloodwork, medical and family history, genetics, and socioeconomic traits impact patient outcomes.
Artificial intelligence (AI) lets us use more of this material than ever. Emerging models can quickly and accurately synthesize enormous amounts of data, predicting potential patient outcomes and helping doctors make calls about treatments or preventive care.
Predictive algorithms hold great promise. Some can diagnose breast cancer with a higher rate of accuracy than pathologistsOther AI tools are already in use in medical settings, allowing doctors to more quickly look up a patient's
