Artificial Intelligence for Health Equity: A Multi-tiered Ml Framework for Tribal Women’s Healthcare and Frontline Worker Empowerment in India

Author(s)	Dr. Abha Sharma, Dr. Denny Ben
Country	India
Abstract	Indian tribal women endure a "triple burden" characterized by geographical isolation, socio-economic marginalization, and deficient healthcare infrastructure [1]. Traditional healthcare delivery systems often fail this demographic due to language barriers, a lack of cultural trust, and a shortage of specialized medical professionals [2]. This paper explores the potential of Artificial Intelligence (AI) and Machine Learning (ML) to bridge these gaps. We propose a decentralized "Human-in-the-Loop" AI framework designed to facilitate early diagnosis, maternal risk stratification, and real-time decision support for frontline health workers, such as ASHAs (Accredited Social Health Activists) and ANMs (Auxiliary Nurse Midwives). By utilizing Edge-AI for offline diagnostics and Natural Language Processing (NLP) for local dialects, this system seeks to reduce maternal mortality and empower the tribal healthcare ecosystem [3].
Keywords	Tribes, Machine learning, healthcare, ASHA workers, prediction, women health
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-25

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International Journal For Multidisciplinary Research