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.

Background: Opioid analgesic therapy has been traditionally used for pain management; however, the variability in patient characteristics, complexity in evaluating pain, availability of treatment within facilities, and U.S. physicians overprescribing opioids have contributed to the current opioid epidemic. Despite large research efforts investigating the patterns of postsurgical pain management and influencing factors, it remains unclear how these overall trends vary across the varying sizes and available resources of academic hospitals, community hospitals, and outpatient surgery centers. The primary aim of this scoping review was to examine the patterns of contemporary postoperative pain management across healthcare settings, including academic medical centers, community hospitals, and outpatient surgery centers. Specifically, this study investigates how prescription practices for opioids, NSAIDs, and acetaminophen are influenced by patient demographics, including sex, race, gender, insurance status, and other social determinants of health (SDoH), to inform equitable and patient-centered pain management strategies. Methods: This study utilized The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and was used as a reference checklist. The Arksey and O’Malley methodological framework was used to guide the review process. To ensure comprehensive coverage, searches were conducted across three major databases: PubMed, Embase, and Cochrane Library. Results: A total of 43 eligible studies were retained for analysis. The highest reported Healthy People 2030 category was Social and community context (n = 39), while the highest reported category of SDoH was age (n = 36). A total of 34 articles listed sex and age as SDoH. Additional SDoH examined were race/ethnicity (n = 17), insurance (n = 7), employment (n = 1), education (n = 4), and income (n = 1). This review suggests that there are significant gaps in the implementation of institution-specific, patient-centered, and equitable pain management strategies, particularly in academic hospitals, which our findings show have the highest rates of opioid and NSAID prescriptions (n = 26) compared to outpatient surgical centers (n = 8). Findings from our review of the literature demonstrated that while academic hospitals often adopt enhanced recovery protocols aimed at reducing opioid dependence, these protocols can fail to address the diverse needs of at-risk populations, such as those with chronic substance use, low socioeconomic status, or racial and ethnic minorities. Conclusions: Findings from this review are expected to have implications for informing both organizational-specific and nationwide policy recommendations, potentially leading to more personalized and equitable pain management strategies across different healthcare settings. These include guidelines for clinicians on addressing various aspects of postoperative pain management, including preoperative education, perioperative pain management planning, use of different pharmacological and nonpharmacological modalities, organizational policies, and transition to outpatient care.

In the United States, overprescribing of antibiotics for viral respiratory infections and antimicrobial resistance continue to be public health concerns. To date, no literature has focused on antibiotic prescribing patterns from free clinics. To address this gap, we used patient-level data from a student-run free clinic network of four primary care clinics to assess factors associated with inappropriate antibiotic prescribing for viral respiratory infections. Treatment plans were deemed inappropriate if any type of antibiotic was prescribed. We used unpaired t tests and chi-square tests to assess for differences in receiving an inappropriate antibiotic prescription by patient-level factors (i.e. age, race/ethnicity, sex, educational attainment, preferred language, insurance status). Of 298 visits, 22.5% did not meet treatment guidelines. No patient-level factors studied were associated with inappropriate antibiotic prescribing. Our findings suggest other factors, beyond those at the patient level, may be drivers of variation in antibiotic prescribing in free clinics.

Background: Student-run free clinics (SRFC) may offer needed mental health care to low income and uninsured populations who may have greater risk of mental illness. To date, there is limited literature assessing what patient-level factors are predictors of referrals to mental health services. Method: Medical record data came from a SRFC network on patients with depression, anxiety, and/or bipolar disorder (n=1,180). The dependent variable was whether patients received a mental health service referral, defined as an accepted internal referral to the study site’s mental health resources or external referral to a community mental health center. We conducted a binary logistic regression model to assess the relationship between various patient-level factors (age, primary language, insurance status, sex, race, ethnicity, and education status) and an accepted mental health referral. Results: Approximately 65.5% of patients received and accepted a mental health referral. Older patients had higher referral odds while males and high school diploma/vocational/associate degree holders had lower odds. There were no statistically significant differences in the odds of receiving a mental health referral by ethnicity, race, insurance status, or primary language. Conclusion: Elderly individuals were more likely to be accept referrals for mental health services, whereas male patients or those with a lower educational attainment were less likely to receive and accept such referrals. Future research should examine the role of patient attitudinal factors (e.g., perceived efficacy of mental health services, treatment preferences) and clinician-level factors on referral to mental health services.

Background: ChatGPT, a large language model artificial intelligence platform that uses natural language processing, has seen its implementation across a number of sectors, notably in health care. However, there remains limited understanding regarding the efficacy of ChatGPT in addressing commonly asked questions on public health subjects. This study aimed to investigate whether ChatGPT could appropriately answer frequently asked questions related to sexually transmitted diseases (STDs). Methods: Ten frequently asked questions on STDs were gathered from 25 different government agency websites. The questions were inputted into ChatGPT, and subsequent responses were analyzed for accuracy, clarity, and appropriateness using an evidence-based approach on a 4-point grading scale. Results: Of the responses provided by ChatGPT, 4 were determined to be excellent requiring no clarification and 6 requiring minimal clarification. No responses were graded as unsatisfactory. Additionally, the responses appropriately emphasized consulting a health care specialist. Conclusion: Although the majority of responses required minimal clarification, ChatGPT has the potential to be an effective supplementary tool for patient education. Additional research is necessary to explore possible public health strategies that incorporate artificial intelligence to address concerns related to STDs.

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