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.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Artificial intelligence (AI), particularly large language models (LLMs), is increasingly used in digital health to support patient engagement and behavior change. One novel application is the delivery of motivational interviewing (MI), an evidence-based, patient-centered counseling technique designed to enhance motivation and resolve ambivalence around health behaviors. AI tools, including chatbots and virtual agents, have shown promise in simulating human-like dialogue and applying MI techniques at scale. However, the extent to which AI systems can faithfully replicate MI principles and generate meaningful behavioral outcomes remains unclear. Objective: This scoping review aimed to assess the scope, characteristics, and findings of existing studies that evaluate AI systems delivering motivational interviewing directly to patients. Specifically, we examined the feasibility of these systems, their fidelity to MI principles, and any reported outcomes related to health behavior change. Methods: We conducted a comprehensive search of five electronic databases (PubMed, Embase, Scopus, Web of Science, and Cochrane Library) for studies published between January 1, 2018, and February 25, 2025. Eligible studies included any empirical design that used AI to perform MI with patients targeting a specific health behavior (e.g., smoking cessation, vaccine uptake). We excluded studies using AI solely for training clinicians in MI. Three independent reviewers conducted screening and data extraction. Extracted variables included study design, AI modality and type, health behavior focus, MI fidelity assessment, and reported outcomes. Data were synthesized narratively to map the evidence landscape. Results: Out of 1001 records identified, 8 studies met the inclusion criteria. Most were exploratory feasibility or pilot studies; only one was a randomized controlled trial. AI modalities included rule-based chatbots, large language models (such as GPT-4), and virtual reality conversational agents. Targeted behaviors included smoking cessation, substance use reduction, vaccine hesitancy, type 2 diabetes self-management, and opioid use during pregnancy. Across studies, AI-delivered MI was rated as usable and acceptable. Patients frequently described AI systems as “judgment-free” and supportive, which enhanced openness and engagement, particularly in stigmatized contexts. Expert evaluations of MI fidelity reported high alignment with MI principles in most cases. However, participants also noted a lack of emotional depth and limited perceived empathy. One study improved these perceptions by adjusting conversational pacing and content complexity. Only one study evaluated behavioral outcomes and found no statistically significant changes. Conclusions: AI systems, particularly those powered by LLMs, show promise in delivering motivational interviewing that is scalable, accessible, and perceived as nonjudgmental. While AI can replicate many structural aspects of MI and foster engagement, current evidence on its efficacy in driving behavior change is limited. More rigorous studies, including randomized controlled trials with diverse populations, are needed to assess long-term outcomes and to refine AI-human hybrid models that balance efficiency with relational depth.

Objectives: This scoping review aims to fill research gaps by exploring four guiding research questions. First, we aim to understand the major barriers encountered by U.S. menstruating college students in accessing menstrual health products at their educational institutions, households, and community. Second, we aim to identify social determinants of health (SDoH) that significantly influence and contribute to menstrual health inequities experienced by U.S. college students. Third, we aim to explore the measurement tools that have been used to assess menstrual health inequities experienced by U.S. menstruating college students. Lastly, we aim to determine the lessons learned and recommendations to improve overall menstrual health outcomes in U.S. menstruating college students. Study Design: This scoping review followed the Arksey and O’Malley framework (2005) and incorporated recommendations from the Joanna Briggs Institute (JBI) for the extraction, analysis, and presentation of results. Methods: The four databases searched were PubMed, Embase (Ovid), Web of Science, and Cochrane Library (Medline). Included articles were (1) published between 2000 and 2023, (2) focused on menstruating college students in the United States, (3) addressed menstrual health, menstrual equity, and period poverty in college campuses in the U.S., and/or (4) explored challenges encountered by menstruating college students in accessing menstrual health products. Results: A total of seven studies were retained for assessment. The most frequently cited Healthy People 2030 categories were economic stability (n = 5) and social and community context (n = 5). The most-cited barriers were sorted into the “Financial Constraints and Accessibility” (n = 13) theme category. Conclusions: Our study highlights practical applications and several recommendations for the future design, adoption, implementation, and evaluation of effective evidence-based interventions to address period poverty and subsequent barriers imposed by menstruating college students specific SDoH. More research is needed to further explore the health implications of menstrual health on mental, physical, and socioeconomic outcomes of menstruating individuals, particularly young adults experiencing different challenges as they venture into college and build their careers.

Background: This systematic review has the following aims: (1) to identify measurement tools used globally by healthcare providers to diagnose PCOS in women at elevated risk; (2) to assess the comprehensiveness of these tools regarding mental health and chronic pain; (3) to list strategies for validating, disseminating, and implementing these tools; and (4) to provide future recommendations for experts in healthcare settings. Methods: This review utilized the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) and the Arksey and O’Malley York methodology. Studies were sourced from the PubMed, Embase, and Cochrane Library databases, with inclusion criteria focusing on peer-reviewed articles addressing PCOS diagnosis and associated comorbidities. Data extraction and analysis followed the Joanna Briggs Institute (JBI) recommendations. Results: A total of 63 studies met the inclusion criteria. Findings indicate that current screening tools for PCOS often lack comprehensive integration of mental health and chronic pain assessments. Tools like the PCOSQ and its updated version, PCOSQ-50, inadequately address pain-related symptoms, highlighting a gap in holistic patient evaluation. This review identified significant associations between PCOS and mental health disorders, including anxiety and depression, emphasizing the need for mental health screenings as part of PCOS management. Conclusions: There is a critical need for validated PCOS screening tools that encompass both physical and psychological aspects of the condition. Educating healthcare providers on the cultural and social determinants influencing PCOS can improve diagnosis and patient outcomes. Future research should focus on developing holistic screening tools and culturally relevant educational resources, aiming to enhance the overall quality of life for women with PCOS.

This case study investigates how a team of librarians at Nova Southeastern University (NSU) worked together to assess and optimize their library's current instructional videos in order to create a mobile-first video hosting platform, known as LibraryLearn. Instructional library videos serve as invaluable resources for students who are not present to partake in synchronous library training. However, once a library has produced a substantial number of videos, the need to organize and routinely update these videos can become a rather daunting challenge, especially when facing obstacles such as loss of original video files, decentralized access points, and viewing incompatibilities among different browsers, operating systems, and mobile devices. In order to enhance user experience and maximize productivity, the development of the LibraryLearn video platform helps to overcome issues regarding usability, accessibility, and incompatibility in order to provide students with “on demand” library instruction.

There is a lack of standardized measurement tools globally to assess knowledge, attitudes, and perceptions of expecting women toward prenatal screening. The purpose of this systematic review was to identify reasons women pursue or decline prenatal screening and compare the strengths and limitations of available measurement tools used to assess pregnant women’s perceptions, knowledge, and attitudes toward prenatal screening. This review followed the five-step York methodology by Arksey and O’Malley and incorporated recommendations from the Preferred Reporting Items for Systematic Reviews and Meta-Analysis checklist for the extraction, analysis, and presentation of results. The five steps consisted of: (1) identification of the research questions; (2) searching for relevant studies; (3) selection of studies relevant to the research questions; (4) data charting; and (5) collation, summarization, and reporting of results. Four online databases (PubMed, Embase, Web of Science, and Cochrane Library) were selected after the librarian’s development of a detailed search strategy. The Rayyan platform was used between June 2023 and August 2023 to epitomize the articles produced from our search. A total of 68 eligible studies were included in the analysis. The top five major reasons for declining prenatal screening uptake included (1) being unsure of the risk of prenatal screening and harm to the baby or miscarriage ( n = 15), (2) not considering action such as termination of pregnancy for prenatal screening to be considered as necessary ( n = 14), (3) high cost ( n = 12), (4) lack of knowledge about testing procedures and being anxious about the test ( n = 10), and (5) being worried about probability of false negative or false positive results ( n = 6). Only 32 studies utilized scientifically validated instruments. Difficulties in capturing representative, adequately sized samples inclusive of diverse ethnicities and demographics were pervasive. Findings highlight the need for rigorous validation of research measurement methodologies to ensure the accuracy and applicability of resulting data regarding the assessment of prenatal screening perceptions, knowledge, and attitudes across diverse female populations. Registration: N/A.