Leveraging AI and Machine Learning for Early Prediction and Prevention of Non- Communicable Diseases in Resource-Limited Settings

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 15 | Issue : 01 | Page : 9 15
    By

    Nishant Prabhudas Mukhiya,

  • Prabhudas G. Mukhiya,

  1. HOD & Associate Professor, Department of Community Medicine, Panchasheel Homoeopathic Medical College & Hospital Khamgaon, Maharashtra, India
  2. HOD & Professor, Department of Repertory and P.G. Director, Panchasheel Homoeopathic Medical College & Hospital Khamgaon, Maharashtra, India

Abstract

Populations in these regions face persistent structural barriers, such as underdeveloped healthcare infrastructure, shortages of trained health professionals, and fragmented or incomplete health information systems. These limitations delay timely diagnosis, restrict access to preventive care, and compromise effective disease management. In recent years, rapid progress in artificial intelligence (AI) and machine learning (ML) has opened promising avenues to mitigate these challenges. Practical applications already emerging include mobile health platforms for community-based screening, AI-assisted diagnostic tools that improve accuracy in low-resource clinics, and ML-driven risk stratification models that allow better prioritization of high-risk patients. Nevertheless, deploying AI and ML solutions in low- and middle-income countries (LMICs) demands careful attention to several critical issues. Ensuring data equity and representation is essential for building reliable models, while generalizability across diverse populations remains a key scientific challenge. Furthermore, the ethical use of AI – encompassing transparency, accountability, and respect for patient privacy – must guide technological adoption. This review provides a comprehensive overview of the current state of AI and ML applications in predicting and preventing NCDs in low-resource contexts. It discusses their potential advantages, implementation challenges, and policy implications, with the aim of informing future research agendas, shaping digital health innovations, and supporting global public health strategies to reduce the burden of NCDs in underserved communities.

Keywords: AI in healthcare, digital health innovations, early detection, health disparities, low- and middle-income countries (LMICs), machine learning applications, non-communicable diseases (NCDs), preventive healthcare, resource-limited settings, risk prediction models

[This article belongs to Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy ]

How to cite this article:
Nishant Prabhudas Mukhiya, Prabhudas G. Mukhiya. Leveraging AI and Machine Learning for Early Prediction and Prevention of Non- Communicable Diseases in Resource-Limited Settings. Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy. 2026; 15(01):9-15.
How to cite this URL:
Nishant Prabhudas Mukhiya, Prabhudas G. Mukhiya. Leveraging AI and Machine Learning for Early Prediction and Prevention of Non- Communicable Diseases in Resource-Limited Settings. Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy. 2026; 15(01):9-15. Available from: https://journals.stmjournals.com/joayush/article=2026/view=237719


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Regular Issue Subscription Review Article
Volume 15
Issue 01
Received 11/02/2026
Accepted 13/02/2026
Published 14/02/2026
Publication Time 3 Days
44

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