Excerpt:

Millions of Americans rely on the internet to answer questions about their own health. The public release of powerful artificial intelligence models like ChatGPT has only accelerated these trends.

In a large survey, more than half of American adults reported putting their own health information into a large language model (LLM). And there’s reason to believe these models might bring real value to these people, such as the case of a mother who, after seeing 17 physicians and receiving no diagnosis for her son with chronic pain, put MRI reports and additional history into ChatGPT. It returned a diagnosis of tethered cord syndrome, which was later confirmed — and operated on — by a neurosurgeon.

This story is not unique. Missed or delayed diagnoses harm patients every day. Each year, an estimated 795,000 Americans die or become permanently disabled from misdiagnoses. And these misdiagnoses are not exclusively rare “zebras” like tethered cord syndrome. Just 15 or so diseases, many of them common, like heart disease and breast cancer, account for half of serious harms. The sicker an individual, the higher the stakes — and the more common these errors become. In a recent study of people admitted to the hospital who were then transferred to an intensive care unit because their conditions got worse, 23% had a diagnostic error affecting their case; 17% of those errors caused severe harm or death.

While numerous factors — many of them outside the control of physicians — are at play in diagnostic errors, human cognition plays a major role. These problems have long been realized by the medical community — the Institute of Medicine released its landmark report “To Err is Human,” in 1999, with comprehensive recommendations to tackle diagnostic errors. But 25 years later, diagnostic errors remain stubbornly persistent.

View full article on STAT