USM Speaker Series on AI and Computation

Our adventure with AI methods in orthopedics: lessons learned

Hilal Maradit Kremers, MD
Mayo Clinic, Rochester, MN

When: October 7th (Thursday), 4-5pm

Zoom Link:

Abstract Total joint arthroplasty (TJA) are the most common and the fastest-growing surgeries in the US, however, the evidence for TJA practice and technologies is based on imperfect and incomplete data. The lack of high-quality phenotypic data is a critical barrier to progress in improving TJA outcomes. Over the past few years, Dr. Hilal Maradit Kremers has established an interdisciplinary team, and have been developing several natural language processing (NLP) and deep learning computer vision algorithms to better phenotype TJA patients. Dr. Maradit will give examples from these projects and outline how the team learned to work together and be productive in developing clinically applicable AI tools in orthopedics.

Dr. Hilal Maradit Kremers is an established musculoskeletal epidemiologist focused primarily on orthopedics research. She is the methodology core director for the NIH- funded core center for clinical research in total joint arthroplasty (American Joint Replacement Research Collaborative – AJRR-C), and her team provides methodological expertise and access to large data resources and expertise to facilitate innovative, methodologically rigorous, and interdisciplinary clinical research in orthopedics.