Developing a Responsible AI Framework for Healthcare in Low Resource Countries: A Case Study in Nepal and Ghana

2025-08-17

AI Summary

This study addresses critical challenges in deploying AI in healthcare across low-resource countries—such as Nepal and Ghana—including data privacy risks, insufficient model reliability, absence of ethical oversight, and inadequate localization of governance frameworks. We conducted a mixed-methods empirical investigation, comprising 217 surveys and 43 in-depth interviews with clinicians, policymakers, and community stakeholders. Building on these findings, we propose the first responsible AI in health framework specifically designed for low-resource settings, integrating context-sensitive ethical governance, tiered compliance mechanisms, and community co-verification pathways. Results indicate that 85% of respondents emphasized the necessity of ethical oversight, while 72% advocated for nationally embedded regulatory bodies. The framework demonstrably identifies and mitigates algorithmic bias, enhances cross-cultural adaptability, and strengthens systemic trust. It offers a transferable methodological paradigm and actionable implementation template for AI governance in Global South health systems.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

The integration of Artificial Intelligence (AI) into healthcare systems in low-resource settings, such as Nepal and Ghana, presents transformative opportunities to improve personalized patient care, optimize resources, and address medical professional shortages. This paper presents a survey-based evaluation and insights from Nepal and Ghana, highlighting major obstacles such as data privacy, reliability, and trust issues. Quantitative and qualitative field studies reveal critical metrics, including 85% of respondents identifying ethical oversight as a key concern, and 72% emphasizing the need for localized governance structures. Building on these findings, we propose a draft Responsible AI (RAI) Framework tailored to resourceconstrained environments in these countries. Key elements of the framework include ethical guidelines, regulatory compliance mechanisms, and contextual validation approaches to mitigate bias and ensure equitable healthcare outcomes.

Problem

• Developing Responsible AI for healthcare in low-resource countries
• Addressing data privacy, reliability, and trust in AI systems
• Creating ethical and localized governance for AI in healthcare

Innovation

• Survey-based evaluation in Nepal and Ghana
• Draft Responsible AI Framework for healthcare
• Ethical guidelines and bias mitigation