Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups. This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation. We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.

Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases.

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model.

Summary: Objective: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements.Methods: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient’s reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model.Results: Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.7310.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN.Conclusions: The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient’s reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.

The impact of a new national kidney allocation system (KAS) on access to the national deceased‐donor waiting list (waitlisting) and racial/ethnic disparities in waitlisting among US end‐stage renal disease (ESRD) patients is unknown. We examined waitlisting pre‐ and post‐KAS among incident (N = 1 253 100) and prevalent (N = 1 556 954) ESRD patients from the United States Renal Data System database (2005‐2015) using multivariable time‐dependent Cox and interrupted time‐series models. The adjusted waitlisting rate among incident patients was 9% lower post‐KAS (hazard ratio [HR]: 0.91; 95% confidence interval [CI], 0.90‐0.93), although preemptive waitlisting increased from 30.2% to 35.1% (P < .0001). The waitlisting decrease is largely due to a decline in inactively waitlisted patients. Pre‐KAS, blacks had a 19% lower waitlisting rate vs whites (HR: 0.81; 95% CI, 0.80‐0.82); following KAS, disparity declined to 12% (HR: 0.88; 95% CI, 0.85‐0.90). In adjusted time‐series analyses of prevalent patients, waitlisting rates declined by 3.45/10 000 per month post‐KAS (P < .001), resulting in ≈146 fewer waitlisting events/month. Shorter dialysis vintage was associated with greater decreases in waitlisting post‐KAS (P < .001). Racial disparity reduction was due in part to a steeper decline in inactive waitlisting among minorities and a greater proportion of actively waitlisted minority patients. Waitlisting and racial disparity in waitlisting declined post‐KAS; however, disparity remains.

Importance: While the literature documenting health disparities has advanced in recent decades, less is known about the pattern of racial/ethnic disparities in emergency care in the United States. Objective: To describe the trends and differences of health outcomes and resource utilization among racial/ethnic groups in US emergency care for adult patients over a 12-year period. Design, Setting, and Participants: This cross-sectional study of emergency department (ED) data from the nationally representative National Hospital Ambulatory Medical Survey (NHAMCS) examined multiple dimensions of ED care and treatment from 2005 to 2016 among adults in the US. Main Outcomes and Measures: The main outcomes include ED care outcomes (hospital admission, ICU admission, and death in the ED/hospital), resource utilization outcomes (medical imaging use, blood test, and procedure use), and patients' waiting time in the ED. The main exposure variable is race/ethnicity including white patients (non-Hispanic), black patients (non-Hispanic), Hispanic patients, Asian patients, and Other. Results: During the 12-year study period, NHAMCS collected data on 247,989 adult (> 18 years old) ED encounters, providing a weighted sample of 1,065,936,835 ED visits for analysis. Asian patients were 1.21 times more likely than white patients to be admitted to the hospital following an ED visit (aOR 1.21, 95% CI 1.12–1.31). Hispanic patients presented no significant difference in hospital admission following an ED visit (aOR 1.01, 95% CI 0.97–1.06) with white patients. Black patients were 7% less likely to receive an urgent ESI score than white patients less likely to receive immediate or emergent scores, as opposed to semi- or non-urgent scores. Black patients were also 10% less likely than white patients to be admitted to the hospital and were 1.26 times more likely than white patients to die in the ED or hospital. Conclusions and Relevance: Race is associated with significant differences in ED treatment and admission rates, which may represent disparities in emergency care. Hispanic and Asian Americans were equal or more likely to be admitted to the hospital compared to white patients. Black patients received lower triage scores and higher mortality rates. Further research is needed to understand the underlying causes and long-term health consequences of these disparities.