ReviewJAMIA open2025
A community-based approach to ethical decision-making in artificial intelligence for health care.
Review in JAMIA open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Implementing a Cognitively Grounded Artificial Moral Advisor: A Multi-LLM Multi-Agent Approach Based on the Cognitive-Reflective Equilibration Model.Journal of Intelligence · 2026Article
- A multimodal, risk-stratified framework for AI-driven early risk prediction and personalised prevention in obesity.Frontiers in artificial intelligence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.