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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.
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OBJECTIVE: Drug-resistant epilepsy can be difficult to cure and may pose emotional challenges for epilepsy providers. Neuropalliative care (NPC) can augment quality of life (QOL) in persons with neurological diseases and may add meaningful elements to the treatment repertoire of epilepsy specialists even if seizures continue. However, NPC has not been widely implemented in epilepsy. Our study aimed to determine whether physicians of persons with drug-resistant epilepsy (PWDRE) experience distress when faced with treatment failure (Engel class ≥ 2), either failure of medications-only (PWDREmo) or of both medications and surgery (procedures with curative intent (PWDREms)). Furthermore, we evaluated physician knowledge about and referrals to NPC following treatment failures to help improve patient QOL despite ongoing seizures. METHODS: An anonymous online survey was distributed to US epilepsy physicians through the American Epilepsy Society website and personal email to assess levels of distress experienced when caring for PWDREmo and PWDREms (7-point Likert scale ["1" = "no distress", "7" = "most distress ever felt"]), and knowledge and use of NPC. RESULTS: Eighty-two physicians completed the survey. Most experienced distress when epilepsy treatments failed: 59% felt moderate distress (≥4) with PWDREmo (median "4", mean 3.74, range 1-7), 90% suffered moderate to severe distress (5, 5.17, 1-7) with PWDREms. Distress over PWDREms was significantly greater than distress over PWDREmo (p < 0.0001). Forty-three percent reported confidence in their knowledge about NPC. Only 15% were likely to refer PWDREmo to NPC, while 44% would consider it for PWDREms. CONCLUSION: Among survey responders, physician distress was high when confronted with treatment failures, especially the failure of epilepsy surgery. Fewer than half of responders were likely to refer patients to NPC. Further research is necessary to determine extent, reasons, and effects of physician distress and whether improved understanding of and patient access to NPC would help alleviate physician distress when faced with treatment failures in PWDRE.
