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The authors say the findings still emphasize the importance of viewing AI tools as complementary resources when seeking medical information.
The study builds on early AI studies showing that chatbots could make diagnoses.
The prediction was feasible across time frames using certain clinical features.
XGBoost was identified as the optimal model, achieving average test AUC of 0.90 before the synthetic minority oversampling technique.
The models perform least effectively for Hispanic and Asian women when evaluating false-positive and false-negative rates.
The greatest effectiveness, however, was seen for young children compared with adults.
Benefits include a significant reduction in mortality and a significant increase in sepsis bundle compliance.
Both specialists and generalists were 4 percentage points less accurate for diagnosis of images of dark versus light skin
Performance was better than that of a certified rater but not quite as good as that of an expert neurologists.
The benefits were seen in neuroimaging and problem-solving, as well as depression and anxiety symptoms.