ArticlePLOS digital health2024
Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities.
Article in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.Balkan medical journal · 2026Review
- The Design-Driven Innovation Path of Human-Centered Artificial Intelligence in the Field of Healthcare: Theory, Practice, and Future Prospects.Healthcare (Basel, Switzerland) · 2026Review
- Governance for safe and responsible AI in healthcare organisations: a scoping review of frameworks.NPJ digital medicine · 2026Article
- Comparing regular expression and machine learning approaches to predict immigrant status from primary care electronic medical record data in Ontario, Canada.PLOS digital health · 2026Article
- Advancing healthcare AI governance through a comprehensive maturity model based on systematic review.NPJ digital medicine · 2026Article
- Exploring opportunities to improve health equity with machine learning and artificial intelligence in healthcare epidemiology.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2026Review
- Advancing health equity in proactive health management: from data underrepresentation and algorithmic bias to a closed-loop governance framework.Frontiers in public health · 2026Review
- Perspectives on healthcare artificial intelligence policy from health equity professionals: findings from an interview study.Frontiers in digital health · 2026Article
- The Health Communications Assessment Checklist: a teaching tool for uncovering bias in the age of AI.Frontiers in public health · 2026Article
- The Emergence of Applied Artificial Intelligence in the Realm of Value Based Musculoskeletal Care.Current reviews in musculoskeletal medicine · 2025Review
- What Is Fair? Defining Fairness in Machine Learning for Health.Statistics in medicine · 2025Review
- Navigating Healthcare AI Governance: the Comprehensive Algorithmic Oversight and Stewardship Framework for Risk and Equity.Health care analysis : HCA : journal of health philosophy and policy · 2025Article
- Evaluating Equity in Usage and Effectiveness of the CONCERN Early Warning System.Applied clinical informatics · 2025Article
- Streamlining medical software development with CARE lifecycle and CARE agent: an AI-driven technology readiness level assessment tool.BMC medical informatics and decision making · 2025Article
- Adoption of artificial intelligence in healthcare: survey of health system priorities, successes, and challenges.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review.PLOS digital health · 2025Article
- State of gender in digital health: where we are and what needs to happen to maximize benefits for all.Women's health nursing (Seoul, Korea) · 2025Article
- Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Leveraging Mobile Health and Wearable Technologies for the Prevention and Management of Atherosclerotic Cardiovascular Disease.Current atherosclerosis reports · 2025Review
- Agenda setting for health equity assessment through the lenses of social determinants of health using machine learning approach: a framework and preliminary pilot study.BioData mining · 2025Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of data-driven technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is growing in healthcare. However, the proliferation of healthcare AI tools has outpaced regulatory frameworks, accountability measures, and governance standards to ensure safe, effective, and equitable use. To address these gaps and tackle a common challenge faced by healthcare delivery organizations, a case-based workshop was organized, and a framework was developed to evaluate the potential impact of implementing an AI solution on health equity. The Health Equity Across the AI Lifecycle (HEAAL) is co-designed with extensive engagement of clinical, operational, technical, and regulatory leaders across healthcare delivery organizations and ecosystem partners in the US. It assesses 5 equity assessment domains-accountability, fairness, fitness for purpose, reliability and validity, and transparency-across the span of eight key decision points in the AI adoption lifecycle. It is a process-oriented framework containing 37 step-by-step procedures for evaluating an existing AI solution and 34 procedures for evaluating a new AI solution in total. Within each procedure, it identifies relevant key stakeholders and data sources used to conduct the procedure. HEAAL guides how healthcare delivery organizations may mitigate the potential risk of AI solutions worsening health inequities. It also informs how much resources and support are required to assess the potential impact of AI solutions on health inequities.
Identifiers
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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.