CTSA hubs are using artificial intelligence (AI) and real-world data (RWD) to advance clinical and translational sciences across a variety of projects. Check out the latest CTSA Program-supported work related to AI and RWD to spark discussions and collaborations at the upcoming 2023 Fall CTSA Program Annual Meeting.
University of Texas Health Sciences Center Houston - Center for Clinical and Translational Sciences
As AI transforms many domains, researchers at the University of Texas Health Sciences Center in Houston set out to focus on its role in clinical and translational research. The aims were threefold: to conduct a scoping literature review, to survey, and to analyze federally funded projects – addressing challenges, successes, and failures with using AI in clinical and translational research. All 63 Clinical and Translational Science Award (CTSA) hubs were surveyed, and 19 of the 63 responded. Learn more about AI focused clinical and translational research being conducted by these hubs.
University of Texas Medical Branch - Institute for Translational Sciences
Researchers at the University of Texas Medical Branch have leveraged AI to determine if the use of calcineurin inhibitors (CNIs) is associated with a reduced prevalence of dementia, including Alzheimer disease. The team used the electronic health records from patients aged 60 or older who were prescribed CNIs within the TriNetX Diamond Network, a longitudinal dataset of linked primary care, medical, and pharmacy claims. Read on to learn more about what the group found regarding CNI use and dementia prevalence.
Boston University – Clinical and Translational Science Institute
Fractures and AI
The U.S. Food and Drug Administration (FDA) has recently published an updated list of 521 authorized AI enabled medical devices, which featured work done by Boston University Medical School. BoneView, a device that uses AI software to detect fractures and traumatic skeletal injuries, was developed by researchers at Boston University CTSI and is the first solution to receive FDA and Continuous Evaluation (CE) clearance in this category. Learn more about how this device will help hospitals reduce missed fractures by 29%.
Hypertension and AI
With a multitude of potential options, finding the right prescription drug for each patient diagnosed with hypertension is a challenge for providers. To help alleviate this issue, researchers at Boston University have co-developed a new artificial intelligence program that may help doctors match patients with the right medicine in real time. Read on to learn more about how this machine learning algorithm could be a game changer for physicians.
University of Arkansas Medical Sciences – Translational Research Institute
Researchers at the University of Arkansas Medical Sciences Translation Research Institute recently developed a learning diagnostic tool for screening patients with common cancers – Heuristic Oncological Prognosis Evaluator (HOPE) 2.0. The tool is the first of its kind, boasting a 95.52% accuracy rate in classifying brain, breast, colorectal, and lung cancer. Prior to HOPE 2.0, no machine learning model had an accuracy of 90% or higher in diagnosing multiple cancers. Read on to learn more about how this breakthrough model will aid in accelerating the diagnosis of multiple cancer types and lead to improved clinical outcomes.
Washington University School of Medicine in St. Louis – Institute of Clinical and Translational Sciences
WashU Medicine, BJC HealthCare, and CuriMeta Partnership
Washington University School of Medicine in St. Louis and BJC Healthcare are partnering with CuriMeta, a company that focuses on accelerating lifesaving research to fight against chronic and acute diseases. The trio will work together using sophisticated datasets in combination with artificial intelligence to predict, prevent, and ultimately cure a variety of diseases. Each partner will bring their own expertise to the table, while utilizing state-of-the-art technologies to ensure patients' identities are kept private. Read on to learn more about what this collaboration plans to accomplish.
Payne Named to National HealthCare AI Code of Conduct Steering Committee
Philip R.O. Payne, Ph.D., has been pinned to help lead the National Academy of Medicine committee on equitable and responsible use of AI in healthcare. Dr. Payne is an inaugural Janet and Bernard Becker Professor and the director of the Institute for Informatics, Data Science & Biostatistics at Washington University School of Medicine in St. Louis. He will be part of the working group to draft a code of conduct for AI in health, medical care, and health research. Learn more about how Dr. Payne plans to keep AI safe, reliable, accurate, and ethical.
Case Western Reserve University – Clinical and Translational Science Collaborative
The Case Western Reserve University Clinical and Translational Science Collaborative of Northern Ohio hosted its first Health Equity Research Bootcamp in August. Participants explored health equity research topics within the infant/maternal morbidity and mortality space where Cleveland has an unfortunate record of ranking high. Notably, participants learned about big data possibilities with TriNetX, a global health research network that uses real-world data to connect industry to study sites. Following the bootcamp, Case Western hosted Big Data Tank: Infant/Maternal Morbidity/Mortality Research Edition, a Shark Tank-inspired research idea pitch night, with a goal of gaining early buy-in and resources to support the publication, dissemination, and implementation of a research study. Read on to learn more about the bootcamp and Big Data Tank.
