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Paper short abstract

This paper analyzes how AI-mediated healthcare in rural Jharkhand, India, reconfigures epistemic hierarchies and reproduces structural inequities. It argues for anthropological frameworks that foreground situated knowledges and challenge universalist assumptions in global health technologies.

Paper long abstract

The expanding integration of Artificial Intelligence (AI) into healthcare governance and clinical decision-making is frequently portrayed as a neutral advance toward efficiency, precision, and universality. Drawing on ethnographic fieldwork and participatory rural appraisal (PRA) conducted among marginalised rural communities in Jharkhand, India, this paper interrogates how AI-mediated healthcare systems intersect with enduring structures of inequality to produce new forms of epistemic exclusion. Rather than treating AI as a purely technical innovation, the paper situates algorithmic technologies within local histories of state neglect, uneven infrastructural development, and stratified access to biomedical authority, contexts that powerfully shape how AI is understood and experienced. I argue that AI in healthcare does not simply redistribute care but reorders whose knowledge is recognised as legitimate in diagnosis, triage, and treatment. Local and embodied understandings of pain, illness, and healing mediated by caste, gender, labour, and precarity often remain illegible to data-driven systems calibrated to distant epistemic and institutional paradigms. In Jharkhand, digital health platforms and algorithmic tools frequently rely on standardised indicators that obscure chronic deprivation, informal care practices, and mobility constraints, reinforcing structural violence through epistemic means. By foregrounding patient encounters, data politics, and everyday negotiations with medical systems, this paper contributes to medical anthropology’s ongoing examination of AI’s role in reconfiguring global health. It calls for frameworks that foreground epistemic plurality and locally situated knowledge, challenging the universalist claims embedded in AI design and envisioning more inclusive, context-sensitive approaches to healthcare technologies.