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.

Two healthcare heavyweights are combining forces to form a technology center they hope will, over the next 10 years, “fundamentally advance the pace” of discovery in medical science and healthcare innovation.

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.

Academic researchers in the U.K. have completed a systematic review of 62 representative studies on the use of AI for COVID-19 diagnostics and prognostics on X-rays and CT scans. Their findings may strike some as a setback.  

The FDA has granted 510(k) clearance to clinical decision support software that uses AI to detect small but potentially cancerous lesions in the lungs.

An AI system for diagnosing prostate cancer on biopsy slides has achieved 98%-plus performance in sensitivity, positive predictive value, specificity and negative predictive value.

The demand for tailored PHI consent for research is strongest among adults 49 and younger, pressing the need to speed the evolution of policies conducive to AI development.

Mayo Clinic has established a new AI-enabled tech platform and spawned two companies to leverage the might of its Big Data inputs.

The aggregated datasets are so complex, applying algorithmic analysis for medical uses may help expand AI as it unlocks biology.

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.