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Google researchers, working with Northwestern Medicine and Jacaranda Health, trained healthcare workers to perform predefined “blind sweep” scans on handheld devices and used a machine learning model to interpret the images. In a study of roughly 2,000 pregnancies in Nairobi and Chicago, the model detected gestational age and fetal presentation with accuracy comparable to trained sonographers.
Researchers at MIT’s McGovern Institute (Daniel Low and Satra Ghosh) built a curated suicide‑risk lexicon linked to 49 risk factors and trained a machine‑learning model on ~16,000 de‑identified Crisis Text Line conversations to classify non‑suicidal, suicidal ideation, and imminent‑risk cases; the work, published in the Journal of Psychopathology and Clinical Science, found mentions of lethal means and substance use were strong predictors of imminent risk. The lexicon contains about 60 words/phrases per risk factor and was curated with clinician input.
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