(4 papers in press)

Regular Issues

• The role of vitamin D supplementation in improving health outcomes among different ethnic groups  
  by Theophilus E. Eboigbe, Shankar Srinivasan 
  Abstract: The study investigates the association between vitamin D supplementation and the risk of diabetes and depression across diverse ethnic groups in the United States, using NHANES 2015-2018 data. The analysis reveals significant ethnic disparities in the protective effects of vitamin D supplementation. Mexican Americans who took supplements had a reduced risk of diabetes (OR = 1.389, 95% CI: 1.142-1.690), while African Americans showed a reduced risk of depression (OR = 1.286, 95% CI:1.021-1.620). These findings suggest that vitamin D supplementation may benefit these populations, likely due to genetic and environmental factors. The study underscores the need for personalised public health strategies that account for ethnic differences and baseline vitamin D levels, advocating for a tailored supplementation approach to mitigate health disparities related to diabetes and depression in high-risk groups.
  Keywords: vitamin D supplementation; diabetes; depression; ethnic disparities; National Health and Nutrition Examination Survey; NHANES; protective effects.
  DOI: 10.1504/IJAIH.2025.10071752
   
  
• Machine learning is revolutionising preventive healthcare and patient monitoring: a review  
  by Yatin Kohli, Monika Kohli, Arun Kohli, M. Uma, Prabhu Sethuramalingam 
  Abstract: Machine learning (ML) integrated with wearable sensors and biosensors transforms healthcare by enabling continuous patient monitoring and early disease detection. These devices collect real-time vital sign data, including blood pressure, heart rate, and glucose levels, to identify patterns and abnormalities. ML algorithms analyse this data to detect chronic conditions like diabetes and cardiovascular diseases before clinical symptoms appear, reducing hospitalisations, emergency visits, and healthcare costs through a proactive approach. Wearable technology enhances personalised medicine by providing patient-specific health insights and actionable recommendations, such as real-time glucose monitoring to help diabetics adjust their diet and medication based on predictive analytics. Additionally, ML-driven systems assess lifestyle factors like activity levels, stress markers, and sleep patterns to predict potential health risks, improving clinical outcomes while optimising healthcare resource utilisation. AI-powered wearable systems ensure continuous adaptation and enhanced diagnostic accuracy over time. The fusion of ML and wearables is shaping the future of healthcare with a focus on personalisation, prevention, and efficiency.
  Keywords: machine learning; ML; artificial intelligence; AI; chronic disease management; telemedicine; remote patient monitoring; RPM.
  DOI: 10.1504/IJAIH.2025.10071764
   
  
• A data-driven prediction of life expectancy trends for 266 countries using time series and polynomial regression models  
  by G. Paavai Anand, K.Aravind Ram, S. Satyavolu Surya Vamsi Krishna 
  Abstract: Life expectancy prediction is vital for policy-making and public health planning. This study applies artificial intelligence to model life expectancy trends across 266 countries using World Bank census data. A hybrid approach combines the auto-regressive integrated moving average (ARIMA) model for capturing linear, time-based trends with polynomial regression to model non-linear residual patterns. The polynomial degree is optimised (1 to 5) to minimise error, enhancing accuracy per country. This dual-model system improves robustness and adaptability to various datasets of the countries, enabling reliable, country-specific predictions. The approach supports informed decision-making for governments and public health bodies worldwide.
  Keywords: ARIMA; mean absolute error; MAE; mean squared error; MSE; polynomial regression.
  DOI: 10.1504/IJAIH.2025.10072522
   
  
• A literature review on artificial intelligence and healthcare management  
  by Esther Hwang, Yujong Hwang 
  Abstract: The purpose of artificial intelligence (AI) is to create an algorithm that functions autonomously to find the solutions to questions. However, the results that AI makes can lead to social biases and other selectivity issues. The social biases include negative statements to ethnic minority groups, gender biases, and cultural biases. Due to this reason, there is a research gap of AI and healthcare management such as AI biases and human-AI interaction. Thus, the goal of this literature review is to comprehensively examine the interaction of AI and users (patients who are in their mid or late-thirties, White, and live in the USA) specifically in the clinical healthcare environment to further enhance the usability of patients and AI.
  Keywords: artificial intelligence; healthcare management; literature review; social bias; gender bias; cultural bias.
  DOI: 10.1504/IJAIH.2025.10072650