Kyu Rhee

Irene Dankwa-Mullan; Kyu Rhee; David M. Stoff; Jennifer Reineke Pohlhaus; Francisco S. Sy; Nathaniel Stinson; John Ruffin

Translational, transdisciplinary, and transformational research stands to become a paradigm-shifting mantra for research in health disparities. A windfall of research discoveries using these 3 approaches has increased our understanding of the health disparities in racial, ethnic, and low socioeconomic status groups. These distinct but related research spheres possess unique environments, which, when integrated, can lead to innovation in health disparities science. In this article, we review these approaches and propose integrating them to advance health disparities research through a change in philosophical position and an increased emphasis on community engagement. We argue that a balanced combination of these research approaches is needed to inform evidence-based practice, social action, and effective policy change to improve health in disparity communities.

Irene Dankwa-Mullan; Marc Rivo; Marisol Sepulveda; Yoonyoung Park; Jane L. Snowdon; Kyu Rhee

Abstract An estimated 425 million people globally have diabetes, accounting for 12% of the worlds health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this article is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers. The authors conducted a predefined, online PubMed search of publicly available sources of information from 2009 onward using the search terms “diabetes” and “artificial intelligence.” The study included clinically-relevant, high-impact articles, and excluded articles whose purpose was technical in nature. A total of 450 published diabetes and AI articles met the inclusion criteria. The studies represent a diverse and complex set of innovative approaches that aim to transform diabetes care in 4 main areas: automated retinal screening, clinical decision support, predictive population risk stratification, and patient self-management tools. Many of these new AI-powered retinal imaging systems, predictive modeling programs, glucose sensors, insulin pumps, smartphone applications, and other decision-support aids are on the market today with more on the way. AI applications have the potential to transform diabetes care and help millions of PWDs to achieve better blood glucose control, reduce hypoglycemic episodes, and reduce diabetes comorbidities and complications. AI applications offer greater accuracy, efficiency, ease of use, and satisfaction for PWDs, their clinicians, family, and caregivers.

Lawrence Blonde; Luigi Meneghini; Xuejun Victor Peng; Anders Boss; Kyu Rhee; Alka Shaunik; Supriya Kumar; Sidhartha Balodi; Claire Brulle-Wohlhueter; Rory J. McCrimmon

Martín-J. Sepúlveda; M. Christopher Roebuck; Paul Fronstin; Pablo Vidales-Calderon; Ashish Parikh; Kyu Rhee

Gwen Hosey; Henry M Ichiho; Dawn Satterfield; Irene Dankwa-Mullan; Stevenson Kuartei; Kyu Rhee; Tayna Belyeu-Camacho; Ione deBrum; Yorah Demei; Kipier Lippwe; Patrick Solidum Luces; Faiese Roby

Aiwen Wu; Peng-ju Chen; Tian-le Li; Irene Dankwa-Mullan; Ting-ting Sun; Kyu Rhee

Daolu Yuwen; Wenwen Zhang; Jiaying Wu; Juan Zhang; Yunzhu Shen; Junfeng Shi; Cuiju Tang; Irene Dankwa-Mullan; Kyu Rhee; Jinfei Chen

Peng-ju Chen; Ting-ting Sun; Tian-le Li; Irene Dankwa-Mullan; Alexandra Urman; Ching-Kun Wang; Yue Zhang; Yunfeng Yao; Guo-li He; Kyu Rhee; Aiwen Wu

Andrew D Norden; Irene Dankwa-Mullan; Alexandra Urman; Fernando Suarez; Kyu Rhee

Jeong-Heum Baek; Sung-Min Ahn; Alexandra Urman; Young Saing Kim; Hee Kyung Ahn; Park Sung Won; Won-Suk Lee; Sun Jin Sym; Heun Kyu Park; Yong-Soon Chun; Eun Kyung Cho; W. Lee; D.B. Shin; Young-Bo Kim; Irene Dankwa-Mullan; Andrew D Norden; Kyu Rhee; Uhn Lee