Data from: 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.