ReviewNPJ digital medicine2025
Bias recognition and mitigation strategies in artificial intelligence healthcare applications.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 3 of them syntheses 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
119 citing papers in PubMed, 3 syntheses or guidelines 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
- Use of large language models for providing automated feedback in medical imaging education: a systematic review.Frontiers in medicine · 2026Pooled it
- Scoping review of precision child and youth mental health research: dwelling in possibility.Frontiers in psychiatry · 2025Pooled it
- Introduction to using symbolic regression for interpretable machine learning in healthcare.Communications medicine · 2026Review
- AI Based Cough Analysis for Pulmonary Tuberculosis Triage and Diagnosis: A Technical Review.npj biomedical innovations · 2026Review
- Neurofeedback for functional neurological disorders: future directions for research and clinical practice.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Review
- Artificial Intelligence (AI) in Home-Care and Community Nursing: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- A Survey on Perspectives Toward Artificial Intelligence Among Italian Interventional Cardiologists.Journal of clinical medicine · 2026Article
- When Do Physicians Trust AI? The Role of Clinical Context in Decision Support: SMART vignette study.Research square · 2026Article
- Role-Differentiated AI Competencies and Curriculum Implications for Health Professions Education: Qualitative Study.JMIR medical education · 2026Article
- Article
- Current state of artificial intelligence applications in assisted reproductive technology: A narrative review.Clinical and experimental reproductive medicine · 2026Article
- Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.International journal of medical informatics · 2026Article
- Harnessing Artificial Intelligence in Health Research in Low-Income and Middle-Income Countries: Potential and Caution.Mayo Clinic proceedings. Digital health · 2026Review
- Artificial Intelligence in Pharmacovigilance: Guiding Principles from the CIOMS Working Group XIV.Drug safety · 2026Review
- Utility of artificial intelligence in predicting clinical outcomes of angiographic procedures: a comprehensive scoping review.Journal of cardiovascular imaging · 2026Review
- Healthcare Digital Twins Across Scales: A Narrative Review and Five-Level Conceptual Framework.Healthcare (Basel, Switzerland) · 2026Review
- Iterative Multidisciplinary Development and Evaluation of a Patient-Facing Social Determinants of Health Chatbot Using Synthetic Data Simulation: Mixed Methods Study.JMIR formative research · 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
- Artificial intelligence in cardio-kidney-metabolic care: Transforming integrated disease management through data-informed innovation.International journal of obesity (2005) · 2026Review
59 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is delivering value across all aspects of clinical practice. However, bias may exacerbate healthcare disparities. This review examines the origins of bias in healthcare AI, strategies for mitigation, and responsibilities of relevant stakeholders towards achieving fair and equitable use. We highlight the importance of systematically identifying bias and engaging relevant mitigation activities throughout the AI model lifecycle, from model conception through to deployment and longitudinal surveillance.
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.