Background: Artificial intelligence (AI) algorithms can be applied in breast cancer risk prediction and prevention by using patient history, scans, imaging information, and analysis of specific genes for cancer classification to reduce overdiagnosis and overtreatment. This scoping review aimed to identify the barriers encountered in applying innovative AI techniques and models in developing breast cancer risk prediction scores and promoting screening behaviors among adult females. Findings may inform and guide future global recommendations for AI application in breast cancer prevention and care for female populations. Methods: The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study. The Arksey and O’Malley methodology was used as a framework to guide this review. The framework methodology consisted of five steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; (5) Collate, summarize, and report the results. Results: In the field of breast cancer risk detection and prevention, the following AI techniques and models have been applied: Machine and Deep Learning Model (ML-DL model) (n = 1), Academic Algorithms (n = 2), Breast Cancer Surveillance Consortium (BCSC), Clinical 5-Year Risk Prediction Model (n = 2), deep-learning computer vision AI algorithms (n = 2), AI-based thermal imaging solution (Thermalytix) (n = 1), RealRisks (n = 2), Breast Cancer Risk NAVIgation (n = 1), MammoRisk (ML-Based Tool) (n = 1), Various MLModels (n = 1), and various machine/deep learning, decision aids, and commercial algorithms (n = 7). In the 11 included studies, a total of 39 barriers to AI applications in breast cancer risk prediction and screening efforts were identified. The most common barriers in the application of innovative AI tools for breast cancer prediction and improved screening rates included lack of external validity and limited generalizability (n = 6), as AI was used in studies with either a small sample size or datasets with missing data. Many studies (n = 5) also encountered selection bias due to exclusion of certain populations based on characteristics such as race/ethnicity, family history, or past medical history. Several recommendations for future research should be considered. AI models need to include a broader spectrum and more complete predictive variables for risk assessment. Investigating long-term outcomes with improved follow-up periods is critical to assess the impacts of AI on clinical decisions beyond just the immediate outcomes. Utilizing AI to improve communication strategies at both a local and organizational level can assist in informed decision-making and compliance, especially in populations with limited literacy levels. Conclusions: The use of AI in patient education and as an adjunctive tool for providers is still early in its incorporation, and future research should explore the implementation of AI-driven resources to enhance understanding and decision-making regarding breast cancer screening, especially in vulnerable populations with limited literacy.

Congenital CMV (cCMV) is the leading cause of hearing loss and neurodevelopmental disabilities in children, affecting 15–18% of births in the United States (U.S.). Despite its strong presence, the lack of routine prenatal screening limits its detection and prevention, especially in low-resource communities. The aim of this scoping review is to describe the scope of the CMV interventions and the policies on CMV screening in the United States, identify successful strategies for the implementation of improved CMV screening rates, and make recommendations for future efforts aimed at improving CMV screening in U.S. healthcare settings. The Joanna Briggs Institute recommendations for scoping reviews and the Arksey and O’Malley (2005) York methodology guided the different review steps. These included (1) identifying research questions; (2) searching for relevant studies; (3) selecting studies relevant to the research questions; (4) charting the data; (5) collating, summarizing, and reporting results. A total of six studies were retained for analysis. A common barrier reported across studies was the lack of generalizability of findings due to small sample size. Challenges encountered by patients often centered around awareness, emotional resistance, and cultural norms. Providers also faced significant barriers, particularly related to time constraints, knowledge gaps, and the prioritization of CMV education in prenatal care. Furthermore, a recurrent theme across included studies was the critical role of education in increasing awareness and reducing CMV transmission risks. Findings will contribute to the advancement of CMV prevention through the identification of gaps in education, policy, and practice, thus assuring better health outcomes for mothers and infants.

There remains a widespread lack of knowledge regarding hidradenitis suppurativa (HS) among physicians in the United States, impeding timely diagnosis and implementation of comprehensive treatment interventions. Despite the presence of supporting communities for affected adolescents and their caretakers, the overall awareness of HS remains low, and a greater consensus on the treatment of HS is needed. Current research highlights the lack of standardized pediatric guidelines for treatment of HS largely due to the varied nature of the disease and limited efficacy of current therapies. Our study aims to explore the relationship between the chronic skin condition HS and social–emotional concerns, mental health, and physical health issues in US children and adolescents. Using the Arksey and O’Malley framework and PRISMA-ScR reporting, we searched PubMed/MEDLINE, Scopus, Web of Science, Cochrane Library, and Embase for U.S. studies (2015–2025) on pediatric HS (<18 years) and social–emotional, mental health, or quality-of-life outcomes. Recommendations were synthesized, and study quality was appraised with CASP checklist methods and rigor. Ten studies (2020–2025) met inclusion criteria. Pediatric HS was associated with depression, anxiety, social withdrawal, shame, low self-esteem, and reduced quality of life. Physical comorbidities increased psychosocial burden. Socioeconomic and racial disparities worsened outcomes and access to care. Studies emphasized early diagnosis, routine screening, multidisciplinary management, and disparity-focused interventions. Findings may inform clinical practice and guide research initiatives aimed at improving outcomes for children and adolescents with HS.