Woodward, Amanda

Health care disparities, particularly among minoritized groups, pose significant challenges within health care systems, including the field of cardiac surgery. Limited English proficiency (LEP) is an often-overlooked factor contributing to these disparities. As key members of the preoperative evaluation, intraoperative management, and postoperative care of cardiac surgery patients, anesthesiologists have both ethical and economic responsibilities to understand, recognize, and address disparities to ensure equitable care for all patients. The objective of this scoping review is to summarize the literature on how LEP impacts the utilization of health services and outcomes after cardiac surgery. More specifically, the review will map how LEP is defined in the literature, summarize the patient populations that have been studied, and describe the health care outcomes in patients with LEP after cardiac surgery. A comprehensive literature search strategy was developed in collaboration with a medical librarian and was registered before conducting the search. Studies were eligible for inclusion in our current study if (i) the patient population was composed of adults (>or = 18), (ii) the study reported health care outcomes before or after cardiac surgery, and (iii) results were stratified by a patient’s English proficiency. All randomized control trials, systematic reviews, observational studies, and cross-sectional studies published in English were included in our study. If conference abstracts met the inclusion criteria, they were included for full-text review. Opinion articles and case reports were excluded. The search produced 2401 articles with 13 articles meeting the inclusion criteria. All studies were conducted in either North America or Australia/New Zealand. The number of patients included in each study ranged from 204 to 21,789, with 4 studies having less than 1500 patients and 2 studies having greater than 10,000 patients. Through a systematic review of the literature on this topic, we identified 3 overarching themes that were inferred from the collective body of studies. First, a significant barrier to studying this topic is the absence of a universal definition of LEP. Second, the heterogeneity in several aspects of the available studies makes it difficult to draw conclusions from the results. Finally, there is a general scarcity of research done on the impact of LEP on cardiac surgery outcomes. Ultimately, our scoping review reveals an area of health disparity research that requires more attention. If disparities are found, then health care leaders may begin investigating which interventions can help mitigate these disparities.

Artificial Intelligence (AI) has emerged as a transformative force in orthopedic oncology, offering significant advances in the diagnosis, classification, and prediction of treatment response for primary malignant bone tumors (PBT). Through machine learning and deep learning techniques, AI leverages computational algorithms and large datasets to enhance medical imaging interpretation and support clinical decision-making. The integration of radiomics with AI enables the extraction of quantitative features from medical images, allowing for precise tumor characterization and the development of personalized therapeutic strategies. Notably, convolutional neural networks have demonstrated exceptional capabilities in pattern recognition, significantly improving tumor detection, segmentation, and differentiation. This narrative review synthesizes the evolving applications of AI in PBTs, focusing on early tumor detection, imaging analysis, therapy response prediction, and histological classification. AI-driven radiomics and predictive models have yielded promising results in assessing chemotherapy efficacy, optimizing preoperative imaging, and predicting treatment outcomes, thereby advancing the field of precision medicine. Innovative segmentation techniques and multimodal imaging models have further enhanced healthcare efficiency by reducing physician workload and improving diagnostic accuracy. Despite these advancements, challenges remain. The rarity of PBTs limits the availability of robust, high-quality datasets for model development and validation, while the lack of standardized imaging protocols complicates reproducibility. Ethical considerations, including data privacy and the interpretability of complex AI algorithms, also warrant careful attention. Future research should prioritize multicenter collaborations, external validation of AI models, and the integration of explainable AI systems into clinical practice. Addressing these challenges will unlock AI’s full potential to revolutionize PBT management, ultimately improving patient outcomes and advancing personalized care.