ReviewNPJ digital medicine2023
Considerations for addressing bias in artificial intelligence for health equity.
Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 140 papers.
What it found
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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.
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Who cites it
140 citing papers in PubMed.
- A vaccine chatbot intervention for parents to improve HPV vaccination uptake among middle school girls: a cluster randomized trial.Nature medicine · 2025Trial
- Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial.Nature communications · 2024Trial
- AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review.JMIR perioperative medicine · 2026Article
- AI Based Cough Analysis for Pulmonary Tuberculosis Triage and Diagnosis: A Technical Review.npj biomedical innovations · 2026Review
- Predicting brain tumour enhancement from non-contrast MRI with artificial intelligence: a multicohort, retrospective, diagnostic accuracy study.The Lancet medical imaging & theranostics · 2026Article
- Assessing body composition via a smartphone computer vision application: High repeatability but method-dependent agreement compared with BODPOD and Inbody.PLOS digital health · 2026Article
- Closing Editorial: Toward Safe, Equitable, and Intelligent Healthcare.Healthcare (Basel, Switzerland) · 2026Article
- Ethics of Autonomous AI Clinical Trials: Delphi Study.Journal of medical Internet research · 2026Article
- Mapping the generative era: a bibliometric and specialty-focused analysis of large language models in healthcare.Visual computing for industry, biomedicine, and art · 2026Article
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Healthcare Digital Twins Across Scales: A Narrative Review and Five-Level Conceptual Framework.Healthcare (Basel, Switzerland) · 2026Review
- I-SCREEN: Development of an AI-based infrastructure for community-wide screening and prediction of progression in age-related macular degeneration providing accessible shared care.Eye (London, England) · 2026Article
- Artificial intelligence in pediatric myopia - a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.Balkan medical journal · 2026Review
- AI Software Among Commercially Insured Populations: Cross-Sectional Study of Patient and Plan Characteristics.JMIR formative research · 2026Article
- Portraying Ethical Risks of Medical AI: Mixed Methods Study From Connotation Definition to a Survey on Physicians' Cognition.Journal of medical Internet research · 2026Article
- Augmenting medical data interpretation with Large Language Models (LLMs): a comparative analysis of patient empowerment, information processing, and technology acceptance.BMC medical informatics and decision making · 2026Article
- Beyond Algorithmic Oversight: Internal Morality of Medicine and Meaningful Human Control in AI-Assisted Care.Healthcare (Basel, Switzerland) · 2026Article
- An empirical study of machine learning robustness and scalability for imbalanced tabular clinical data in emergency and critical care.Scientific reports · 2026Article
- Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review.Public health in practice (Oxford, England) · 2026Review
80 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Health equity is a primary goal of healthcare stakeholders: patients and their advocacy groups, clinicians, other providers and their professional societies, bioethicists, payors and value based care organizations, regulatory agencies, legislators, and creators of artificial intelligence/machine learning (AI/ML)-enabled medical devices. Lack of equitable access to diagnosis and treatment may be improved through new digital health technologies, especially AI/ML, but these may also exacerbate disparities, depending on how bias is addressed. We propose an expanded Total Product Lifecycle (TPLC) framework for healthcare AI/ML, describing the sources and impacts of undesirable bias in AI/ML systems in each phase, how these can be analyzed using appropriate metrics, and how they can be potentially mitigated. The goal of these "Considerations" is to educate stakeholders on how potential AI/ML bias may impact healthcare outcomes and how to identify and mitigate inequities; to initiate a discussion between stakeholders on these issues, in order to ensure health equity along the expanded AI/ML TPLC framework, and ultimately, better health outcomes for all.
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.