Promoting Artificial Intelligence for Global Breast Cancer Risk Prediction and Screening in Adult Women: A Scoping Review.

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.

Digital Health Interventions for Cancer Prevention Among Racial and Ethnic Minority Groups in the United States: A Scoping Review.

OBJECTIVE: The COVID-19 pandemic abruptly accelerated the use of digital health for cancer care. Previously, researchers identified a variety of digital health interventions for cancer prevention. The purpose of the present scoping review was to identify digital health interventions for cancer prevention designed for racial/ethnic minority groups. METHODS: The scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and was guided by the Arksey and O'Malley methodological framework. A search of PubMed, Ovid MEDLINE, and CINAHL for peer-reviewed research articles published from database inception to August 21, 2023, was conducted. Peer-reviewed studies published in English that employed digital health interventions for cancer prevention, that were conducted among racial/ethnic minority groups, and that were conducted in the United States were included. Also included were cancer prevention interventions for people who did not have cancer, people who did have cancer, and cancer survivors. Excluded were interventions that included non-Hispanic White individuals, interventions performed outside the United States, interventions that combined face-to-face methods with digital strategies, and interventions that did not clearly include digital health. Articles that focused on technologies for collecting and transmitting health data (e.g., remote patient monitoring) without an explicit tie-in to cancer prevention intervention outcomes were also excluded. RESULTS: Following screening, eight articles met the eligibility criteria. Six of the articles were published prior to the COVID-19 pandemic, and two were published during it. The digital health interventions for cancer prevention in racial/ethnic minority groups included screening (n = 5), emotional support and education (n = 1), human papillomavirus vaccination (n = 1), and education and treatment (n = 1). A consistently measured outcome was intervention efficacy. Four authors explicitly stated that theories or theoretical constructs were employed to guide intervention development. Also, no interventions were created using novel devices such as emerging technologies. CONCLUSIONS: We identified several notable gaps regarding digital health for cancer prevention among racial/ethnic minority groups. Addressing these gaps may help guide continued innovation in the use of digital health for cancer prevention among racial/ethnic minority groups.

Social determinants of health and health inequities in breast cancer screening: a scoping review.

Introduction: This scoping review aims to highlight key social determinants of health associated with breast cancer screening behavior in United States women aged ≥40 years old, identify public and private databases with SDOH data at city, state, and national levels, and share lessons learned from United States based observational studies in addressing SDOH in underserved women influencing breast cancer screening behaviors. Methods: The Arksey and O'Malley York methodology was used as guidance for this review: (1) identifying research questions; (2) searching for relevant studies; (3) selecting studies relevant to the research questions; (4) charting the data; and (5) collating, summarizing, and reporting results. Results: The 72 included studies were published between 2013 and 2023. Among the various SDOH identified, those related to socioeconomic status (n = 96) exhibited the highest frequency. The Health Care Access and Quality category was reported in the highest number of studies (n = 44; 61%), showing its statistical significance in relation to access to mammography. Insurance status was the most reported sub-categorical factor of Health Care Access and Quality. Discussion: Results may inform future evidence-based interventions aiming to address the underlying factors contributing to low screening rates for breast cancer in the United States.

Using Patient-Centered Dissemination and Implementation Frameworks and Strategies in Palliative Care Settings for Improved Quality of Life and Health Outcomes: A Scoping Review.

BACKGROUND: There is a need for patient-provider dissemination and implementation frameworks, strategies, and protocols in palliative care settings for a holistic approach when it comes to addressing pain and other distressing symptoms affecting the quality of life, function, and independence of patients with chronic illnesses. The purpose of this scoping review is to explore patient-centered D&I frameworks and strategies that have been adopted in PC settings to improve behavioral and environmental determinants influencing health outcomes through evidence-based programs and protocols. METHODS: The five step Arksey and O'Malley's (2005) York methodology was adopted as a guiding framework: (1) identifying research questions; (2) searching for relevant studies; (3) selecting studies relevant to the research questions; (4) charting the data; and (5) collating, summarizing, and reporting results. RESULTS: Only 6 out of the 38 (16%) included studies applied a D&I theory and/or framework. The RE-AIM framework was the most prominently cited (n = 3), followed by the Diffusion of Innovation Model (n = 2), the CONNECT framework (n = 1), and the Transtheoretical Stages of Change Model (n = 1). The most frequently reported ERIC strategy was strategy #6 "Develop and organize quality monitoring systems", as it identified in all 38 of the included studies. CONCLUSION: This scoping review identifies D&I efforts to translate research into practice in U.S. palliative care settings. Results may contribute to enhancing future D&I initiatives for dissemination/adaptation, implementation, and sustainability efforts aiming to improve patient health outcomes and personal satisfaction with care received.