Emergency physicians have a tough time identifying patients who have Crohn’s disease and truly need a CT scan to pinpoint the cause of acute abdominal distress.

The deep learning models had nothing more to go on than routine abdominal CT scans acquired preoperatively.

Working with 9/11 responders as retrospective subjects, researchers in mental health and computer science have shown AI language tools can help predict the course of PTSD.

Comparing four methods for predicting septic shock in children hospitalized with sepsis, Johns Hopkins researchers have found a newer machine-learning approach superior to an older one as well as to two conventional methods.

Psychology researchers have demonstrated a way to finetune diagnoses of major depression and generalized anxiety disorder by analyzing freely elaborated thoughts and feelings using machine learning and natural language processing.

Researchers have developed a nanopore that uses AI to detect COVID-19 and other viruses in easily obtained saliva specimens.

Clinical nutritionists won’t be left out of the medical AI revolution, as researchers are exploring use cases for augmented diet optimization, food image recognition, risk prediction and diet pattern analysis.

Sifting the literature for real-world challenges thwarting adoption of clinical AI across medicine, a team of biomedical engineers and computer scientists has identified and fleshed out an exemplary use case.