Researchers in Beijing built logistic regression and XGBoost models that predict bone scintigraphy diagnostic categories before scanning, finding that probability calibration restores reliability ...

Machine learning models trained on history, bedside examination and vestibular tests can distinguish posterior circulation ...

A machine learning model combining acoustic speech features with PHQ-9 responses improved depression screening accuracy in adolescents.

Detecting impaired motor function as early as possible after birth with the help of a standard camera linked to intelligent ...

Utilizing machine learning to assess distinct depressive symptoms improves the identification of adults with suicidal ideation.

NHS data scientist Olayinka Jimoh develops AI tool to identify patients at risk of serious gastrointestinal bleeding, using ...

A new study analyzing a very large real-world dataset of Nerivio app users suggests that the AI model can predict next-day ...

Detecting impaired motor function in infants using AI-driven motion capture is producing promising results. A French research ...

Machine learning models showed moderate performance in predicting acute and late radiation-induced skin reactions in rectal cancer.

Peer-reviewed study of 53,000 Nerivio users finds a patient's own headache pattern over the past 30 days, not the prodromal symptoms clinicians have watched for decades, is the strongest warning sign ...

A study developed ultrasound radiomics models to distinguish DKD from NDKD in patients with Type 2 diabetes. An integrated ...

Unlocking healthcare data requires more than algorithms—it demands validation, context, and clinical collaboration.