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.

In December 2008, the National Institutes of Health (NIH) sponsored the first NIH Summit showcasing its investment and contribution to health disparities research and unveiling a framework for moving this important field forward. The Summit, titled "The Science of Eliminating Health Disparities," drew on extensive experience of experts leading health disparities research transformation in diverse fields. The Summit also provided a historic educational opportunity to contribute to health care reform. The theme, addressing disparities through integration of science, practice, and policy, introduced a paradigm for advancing research through transformational, translational, and transdisciplinary research. Engaging active participation throughout the Summit generated recommendations bridging science, practice, and policy, including action on social determinants of health, community engagement, broad partnerships, capacity-building, and media outreach.

An estimated 425 million people globally have diabetes, accounting for 12% of the world's 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.