A novel AI-based model for clinical decision support has bested established machine-learning models at predicting how patients with type-2 diabetes mellitus will respond to various categories of therapeutic drugs.

Researchers have developed an AI system that can differentiate primary tumors from metastatic lookalikes on routine histology slides while also helping pinpoint the sites from which the cancer sprung.

Yale researchers have demonstrated a machine learning tool for choosing between coronary imaging and stress testing in patients who present with suspected coronary artery disease.  

Biases in algorithms can have critical implications for minority patients, which is why IBM Research and Watson Health sought out to examine the best methods for addressing this problem.

The newly developed tool predicts the location of accessory pathways inside the heart that can cause irregular heart pulses.

Researchers have used machine learning to predict wellbeing—not only objective physical condition but also subjective overall health—as a function of demographic, socioeconomic and geographic factors.

Johns Hopkins researchers have used deep neural networks to draw important insights—prescriptive as well as descriptive—into adaptive immunity from massive stores of T-cell receptor sequencing data.

Noting that multiple sclerosis now affects more people between 50 and 60 than any other age group, researchers have shown how machine-learning gait analysis can help personalize therapy regimens.