Kristin Higgins

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About

Kristin Higgins is a leading researcher at the intersection of medical imaging and artificial intelligence, with a primary focus on point-of-care ultrasound (POCUS) and its application in critical care. Her most influential work, the 2022 review "Machine Learning in Lung Ultrasound in COVID-19 Pandemic," has garnered 56 citations, highlighting its pivotal role during the global health crisis. In this paper, Higgins synthesized the rapid integration of machine learning algorithms with lung ultrasound (LUS) to diagnose COVID-19-associated pneumonia and acute respiratory distress syndrome (ARDS). Her major contribution lies in demonstrating how AI-enhanced POCUS can serve as a rapid, non-invasive diagnostic tool, reducing reliance on traditional imaging in overwhelmed clinical settings. By evaluating the efficacy of deep learning models in interpreting LUS images, she provided a roadmap for deploying automated triage systems during pandemics. Higgins’ work is notable for bridging the gap between bedside clinical practice and computational innovation, offering scalable solutions for resource-limited environments. Her research continues to shape the future of tele-ultrasound and AI-assisted diagnostics, making her a key figure in the evolution of modern critical care imaging.