ArticleNPJ digital medicine2025
A scoping review and evidence gap analysis of clinical AI fairness.
Article 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 18 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Use of Machine Learning in Emergency Care Units: A Systematic Review.Journal of primary care & community healthPooled it
- Clinical Performance and Implementation of AI-Enabled Paediatric Ophthalmic Screening, Triage, Diagnosis, and Surveillance in Primary, Community, and Referral-Linked Pathways: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 2026Article
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Article
- Responsible AI for Predicting Delayed Hospital Discharge Among Older Adults: Development and Evaluation Study for Balancing Accuracy, Equity, and Explainability.JMIR medical informatics · 2026Article
- Improving Fairness and Mitigating Bias in Multicenter Electronic Health Records Models to Predict Glaucoma Outcomes.Ophthalmology science · 2026Article
- Then and Now: What We Have Learned From the WHI.The Journal of clinical endocrinology and metabolism · 2026Review
- Realising the digital twin: a thematic review and analysis of the ethical, legal, and social issues for digital twins in healthcare.AI & society · 2026Article
- Advancing health equity in proactive health management: from data underrepresentation and algorithmic bias to a closed-loop governance framework.Frontiers in public health · 2026Review
- Article
- Twelve tips for developing and implementing AI curriculum for undergraduate medical education.Medical education online · 2025Review
- Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models.NPJ breast cancer · 2025Review
- AI-augmented frameworks for enhancing Alzheimer's disease clinical trials: A memory clinic perspective.The journal of prevention of Alzheimer's disease · 2025Article
- Unlocking the potential of real-time ICU mortality prediction: redefining risk assessment with continuous data recovery.NPJ digital medicine · 2025Article
- Large language model bias auditing for periodontal diagnosis using an ambiguity-probe methodology: a pilot study.Frontiers in digital health · 2025Article
- AI testing, evaluation, verification and validation for accessibility: a comprehensive framework.Frontiers in digital health · 2025Article
- Beyond gender and racial bias: Towards pro-justice ethical GenAI use in medicine and health.Women's health (London, England)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ethical integration of artificial intelligence (AI) in healthcare necessitates addressing fairness. AI fairness involves mitigating biases in AI and leveraging AI to promote equity. Despite advancements, significant disconnects persist between technical solutions and clinical applications. Through evidence gap analysis, this review systematically pinpoints the gaps at the intersection of healthcare contexts-including medical fields, healthcare datasets, and bias-relevant attributes (e.g., gender/sex)-and AI fairness techniques for bias detection, evaluation, and mitigation. We highlight the scarcity of AI fairness research in medical domains, the narrow focus on bias-relevant attributes, the dominance of group fairness centering on model performance equality, and the limited integration of clinician-in-the-loop to improve AI fairness. To bridge the gaps, we propose actionable strategies for future research to accelerate the development of AI fairness in healthcare, ultimately advancing equitable healthcare delivery.
Identifiers
What Socratic 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 Socratic graph.