The new center, supported by a $5 million grant from the Agency for Healthcare Research and Quality and the Patient-Centered Outcomes Research Institute will focus on training scientists and supporting research to minimize gaps between the generation of clinical evidence, implementation of proven interventions and development of informed public health policy.

Vanderbilt University Medical Center physicians and researchers are applying artificial intelligence in innovative ways to advance clinical care and scientific understanding of these cutting-edge tools.

An automated solution for creating phenotyping algorithms, PheNorm, worked well to identify symptomatic COVID-19 cases in electronic health records, suggesting that automation could speed high-throughput phenotyping of acute disease.

A Vanderbilt team has received funding to pursue research projects deploying artificial intelligence (AI) and machine learning (ML) algorithms to enhance diagnostic decision-making in health care.

A Vanderbilt study found that automated suicide risk prediction models operating on electronic health records could help clinical teams efficiently identify patients for face-to-face suicide risk screening and prevention.

A study by Vanderbilt researchers found that more than half of the patients who receive the results of genetic tests might not be impelled to seek more services than they're already receiving.

Artificial intelligence has the potential to transform the practice of medicine but, like any other new tool or method, it needs to be rigorously validated before it is widely applied, cautions Vanderbilt's Dan Roden, MD.