In a recent cross-sectional study, machine learning models were examined, showing similar performance in identifying patients at high risk for GIB after being prescribed antithrombotic agents. Findings have been published in JAMA Network Open. "Two models (RegCox and XGBoost) performed modestly better than the HAS-BLED score. A prospective evaluation of the RegCox model compared with HAS-BLED may provide a better understanding of the clinical impact of improved performance."the Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic treatment (oral antiplatelets or anticoagulants) is limited by existing risk prediction models.

Researchers are studying whether artificial-intelligence tools that analyze things like typing speed, sleep patterns and speech can be used to help clinicians better identify patients with early-stage dementia. Huge quantities of data reflecting our ability to think and process information are now widely available, thanks to watches and phones that track movement and heart rate, as well as tablets, computers and virtual assistants such as Amazon Echo that can record the way we type, search the internet and pay bills.