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

Abstract

Objectives: 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 article introduces the AI COmmunity-based Ethical Dialogue and DEcision-making (CODE) framework to embed ethical deliberation into AI development, focusing on Electronic Health Records (EHRs).

Materials and methods: 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.

Results: The framework outlines 5 steps to advance health equity: (1) Contextual diversity and priority: Ensuring inclusive datasets and that AI reflects the community needs; (2) Sharing ethical propositions: Structured discussions on privacy, bias, and fairness; (3) Dialogic decision-making: Collaboratively with stakeholders to develop AI solutions; (4) Integrating ethical solutions: Applying solutions into AI design to enhance fairness; and (5) Evaluating effectiveness: Continuously monitoring AI to address emerging biases.

Discussion: 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.

Conclusion: The framework provides practical guidance to ensure AI systems are ethical, community-driven, and aligned with health equity goals.

Keywords: artificial intelligence; community involvement; decision making; electronic health record data; health care ethics.