Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field’s inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI’s application in healthcare.

Background: We aimed to assess the association of sleep with metabolic syndrome in the 2013/2014 National Health and Nutrition Examination Survey (NHANES). Methods: Sample size included 2737 out of 2013 and 2014 NHANES surveys. Cross-sectional study of metabolic syndrome and sleep duration was conducted. Metabolic syndrome was defined according to NCEP ATPIII (National Cholesterol Education Program Adult Treatment Panel III) criteria. Metabolic syndrome severity score was calculated based on actual measurement of each component, adjusted for sex and race. The generalized additive model (GAM) was built to assess the smooth relationship between metabolic syndrome/metabolic syndrome severity score and sleep duration. Adjustment of models were done for age, sex, race, and sitting time. The value of effective degree of freedom (EDF) formed by the GAM model shows the degree of curvature of the relationship. A value of 1 for EDF is translated as the linear shape of relationship. Values larger than one denote a more complex relationship between the response variable and the predicting one. Results: There was a U-shaped association between sleep duration and metabolic syndrome in univariable GAM (EDF = 2.43, p = 0.06) and multivariable GAM (EDF = 2.03, p = 0.20). The lowest risk of metabolic syndrome was observed in people sleeping 7 hours/night. There was a significant U-shaped association between sleep duration and metabolic syndrome severity score in multivariable GAM (EDF = 2.94, p = 0.0004). Similarly, the lowest mean metabolic syndrome severity score was observed in people sleeping 7 hours/night. There was an effect modification of sex and sleep duration indicating strong U-shaped relationship of metabolic syndrome severity score and sleep duration in women (EDF = 3.43, p = 0.00002) and semi-linear association in men (EDF = 1.76, p = 0.04). Conclusion: Short and long sleep duration was associated with higher risk of metabolic syndrome and higher scores of metabolic syndrome severity score in women. Short sleep duration was associated with higher risk of metabolic syndrome and higher scores of metabolic syndrome severity score in men.

Background: Barriers to breast cancer screening remain despite Medicaid expansion for preventive screening tests and implementation of patient navigation programs under the Affordable Care Act. Women from underserved communities experience disproportionately low rates of screening mammography. This study compares barriers to breast cancer screening among women at an inner-city safety-net center (City) and those at a suburban county medical center (County). Inner city and suburban county medical centers’ initiatives were studied to compare outcomes of breast cancer screening and factors that influence access to care. Methods: Women 40 years of age or older delinquent in breast cancer screening were offered patient navigation services between October 2014 and September 2019. Four different screening time-to-event intervals were investigated: time from patient navigation acceptance to screening mammography, to diagnostic mammography, to biopsy, and overall screening completion time. Barriers to complete breast cancer screening between the two centers were compared. Results: Women from lowest income quartiles took significantly longer to complete breast cancer screening when compared to women from higher income quartiles when a barrier was present, regardless of barrier type and center. Transportation was a major barrier to screening mammography completion, while fear was the major barrier to abnormal screening work up. Conclusion: Disparity in breast cancer screening and management persists despite implementation of a patient navigation program. In the presence of a barrier, women from the lowest income quartiles have prolonged breast cancer screening completion time regardless of center or barrier type. Women who experience fear have longest screening time completion. Future directions aim to increase resource allocation to ameliorate wait times in overburdened safety-net hospitals as well as advanced training for patient navigators to alleviate women’s fears.