An AI-based sepsis screening tool could better help physicians find patients most at risk of developing the illness after it outperformed other traditional screening methods, according to a study published in the Annals of Emergency Medicine.

Sepsis is a leading cause of mortality in U.S. hospitals. However, most of these deaths are unlikely to be prevented with better care in hospitals, according to a new study published in JAMA Network Open. 

Multinational investment holding conglomerate Tencent is working with the Shanghai Cancer Center to establish an AI-based, big data laboratory to streamline the cancer treatment process, according to a report by Shine, a digital news organization under Shanghai Daily.

Researchers have developed a machine learning system that predicts the severity of arthritis in a pediatric population, allowing for treatment to be personalized. Findings of their research were published in PLOS Medicine.

As the number of diabetes cases continues to rise in India, Google and Verily, the life sciences research organization under Alphabet, have a solution to better screen for the disease and associated eye diseases.

Researchers at the University of Alberta have developed a new AI-based software––Ensemble Algorithm with Multiple Parcellations for Schizophrenia Prediction, or EMPaSchiz––that will allow physicians to identify schizophrenia in fMRI scans with 87 percent accuracy. Their research was published in NJP Schizophrenia.

Dana Solutions, a California-based AI company, is partnering with the FDA’s Center for Drug Evaluation and Research to improve the drug development process by using AI, the company announced Feb. 25.

A new machine learning model allows for physicians to determine whether atypical ductal hyperplasia (ADH) could upgrade to cancer, according to new research published in JCO Clinical Cancer Informatics. The model can identify 98 percent of all malignant cases prior to surgery, while sparing 16 percent of women from unnecessary surgeries.