Understanding and Addressing Prostate Cancer Disparities in Diagnosis, Treatment, and Outcomes Among Black Men.

Despite advances in screening, diagnosis, and treatment for prostate cancer (PCa), Black men tend to be diagnosed at younger ages, have higher mortality rates, and are at increased risk of recurrence or metastasis compared to their White counterparts. PCa disparities among Black men are caused by a complex interaction of social, behavioral, and biological factors across the public policy, community, organizational, interpersonal, and individual levels. Key contributing factors include mistrust in the health care system, poor communication between patients and providers, low awareness of screening guidelines, and high medical costs. These disparities are further exacerbated by the low representation of Black men in clinical trials, which limits access to high-quality cancer care and generalizability for PCa treatments. In this narrative review of the existing literature, we examined the epidemiology and identified contributing factors, and propose multi-level strategies to address and mitigate disparities among Black men with PCa.

Bridging Health Disparities in the Data-Driven World of Artificial Intelligence: A Narrative Review.

BACKGROUND: Artificial intelligence (AI) holds exciting potential to revolutionize healthcare delivery in the United States. However, there are concerns about its potential to perpetuate disparities among historically marginalized populations. OBJECTIVE: Following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses, we conducted a narrative review of current literature on AI and health disparities in the United States. We aimed to answer the question, Does AI have the potential to reduce or eliminate health disparities, or will its use further exacerbate these disparities? METHODS: We searched the Ovid MEDLINE electronic database to identify and retrieve publications discussing AI and its impact on racial/ethnic health disparities. Articles were included if they discussed AI as a tool to mitigate racial health disparities with or without bias in developing and using AI. RESULTS: This review included 65 articles. We identified six themes of limitations in AI that impact health equity: (1) biases in AI can perpetuate and exacerbate racial and ethnic inequities; (2) equity in algorithms should be a priority; (3) lack of diversity in the field of AI is concerning; (4) the need for regulation and testing algorithms for accuracy; (5) ethical standards for AI in health care are needed; and (6) the importance of promoting transparency and accountability. CONCLUSIONS: While AI promises to enhance healthcare outcomes and address equity concerns, risks and challenges are associated with its implementation. To maximize the use of AI, it must be approached with an equity lens during all phases of development.