A community-based approach to ethical decision-making in AI for health care

Artificial Intelligence (AI) is transforming healthcare by improving diagnostics, treatment recommendations, and resource allocation. However, its implementation also raises ethical concerns, particularly regarding biases in AI algorithms trained on inequitable data, which may reinforce health disparities. This paper introduces the AI CODE (COmmunity-based Ethical Dialogue and DEcision-making) framework to embed ethical deliberation into AI development, focusing on Electronic Health Records (EHRs). We propose the  AI CODE framework as a structured approach to addressing ethical challenges in AI-driven healthcare and ensuring its implementation supports health equity. To develop this framework, we conducted a narrative synthesis of case studies from the literature that discussed ethical challenges and proposed solutions in applying AI to EHR datasets, as well as an analysis of current AI-related regulations. We examine the framework’s role in mitigating AI biases through structured community engagement and its relevance within evolving healthcare policies. While the framework promotes ethical AI integration in healthcare, it also faces challenges in implementation. The framework provides practical guidance to ensure AI systems are ethical, community-driven, and aligned with health equity goals.