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Wrong automated advice cuts clinicians' accuracy, meta-analysis finds

Physician in the Loop, Oct 24, 2026

Illustration: a bar chart on paper with a glowing check mark stamp and a wooden rubber stamp on a wooden table.

Clinicians had about 10 times the odds of a correct decision when automated advice was right than when it was wrong, a meta-analysis of 46 reports published Oct. 23 in npj Digital Medicine found.

In four real-world studies of computerized electrocardiogram readings, 26.6% of erroneous machine interpretations went uncorrected and unrecognized, the authors reported. Studies of current AI and large language models documented harmful reliance on wrong output but were too varied to pool, and only one study reported harm at the patient level. Human oversight is protective "only if clinicians recognize and resist erroneous output," wrote the authors, from West China Hospital at Sichuan University.

Elsewhere, Quest Diagnostics told regulators an unauthorized party reached patient records through the Quanum system clinicians use to order lab tests, and the Department of Health and Human Services moved to keep its workforce on Anthropic's Claude without a competitive bid.

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