In recent years, a wealth of literature has emerged exploring how AI and machine learning (ML) can improve diagnostic precision in medicine. Combined with deep learning (a subset of ML), this research has the potential, inter alia, to advance cancer detection, streamline treatment algorithms, and enhance our ability to predict
Of all the healthcare topics in vogue these days, the phrase “social determinants of health” (SDOH) has enjoyed an increasingly prominent place in both practice and policy. First inspired by Geoffrey Rose’s 1992 book, The Strategy for Preventative Medicine, and then mainstreamed by the WHO in the early 2000s,
Back in March 2020, a University of Pittsburgh physician by the name of Norman C. Wang published an article in the Journal of the American Heart Association (JAHA) about the use of race and ethnicity considerations when recruiting for the US cardiology workforce. Wang argued that Diversity, Equity, and Inclusivity
Like many professions in Western society, medicine is examining itself for the presence of racial inequities and strategies that can ameliorate these differences. Many publications have focused on the disproportionately poor outcomes of minorities in our healthcare system with an emphasis on systemic and structural forces that shape such inequities.
In the last few decades, the proliferation of diversity, inclusivity, and equity literature throughout the medical profession has become institutionalized. Medical organizations such as the World Health Organization (WHO), the National Institute of Health (NIH), and the American College of Cardiology (ACC) have embraced this ideology and its accompanying bureaucracies