Artificial intelligence supports triage of eye patients cared for through telehealth, interprets eye images, and assesses health risks based on retina photos. The goal is to improve diagnosis of a variety of conditions, including glaucoma, macular degeneration, and diabetic retinopathy.

This IRB approved research study uses  a randomized trial for finding colon polyps with artifical intelligence.

This pilot project uses TIU documentation on African American Veterans aged 45-50 to extract family medical history data and identify Veterans who are are at risk of prostate cancer but have not undergone prostate cancer screening.

The Medtronic GI Genius aids in detection of colon polyps through artificial intelligence.

This study is investigating the use of artificial intelligence models for improving clinical management of colorectal polyps. The models receive video frames from colonoscopy video streams and analyze them in real time in order to (1) detect whether a polyp is in the frame and (2) predict the polyp's malignant potential.

This prognostic study used data on patients with hepatitis C virus (HCV)-related cirrhosis in the national Veterans Health Administration who had at least 3 years of follow-up after the diagnosis of cirrhosis.

A machine learning model is used to predict disease progression among veterans with hepatitis C virus.

Machine learning analyzes patient demographics, medication use, and longitudinal laboratory values collected between 2001 and 2015 from adult patients in the Veterans Integrated Service Networks (VISN) 10 cohort. The data was used for analysis in prediction of Crohn’s disease and to model future surgical outcomes within 1 year.

This work uses random forest modeling on a cohort of 594 patients with Vedolizumab to predict the outcome of corticosteroid-free biologic remission at week 52 on the testing cohort. Models were constructed using baseline data or data through week 6 of VDZ therapy.

This work examines data from 20,368 Veterans Health Administration (VHA) patients with an irritable bowel disease (IBD) diagnosis between 2002 and 2009. Longitudinal labs and associated predictors were used in random forest models to predict hospitalizations and steroid usage as a surrogate for IBD Flares.