Forthcoming and Online First Articles

International Journal of Artificial Intelligence in Healthcare

International Journal of Artificial Intelligence in Healthcare (IJAIH)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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(2 papers in press)

Regular Issues

  • The role of vitamin D supplementation in improving health outcomes among different ethnic groups   Order a copy of this article
    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   Order a copy of this article
    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
     

 

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