ReviewNPJ digital medicine2023
A translational perspective towards clinical AI fairness.
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 46 papers, 1 of them a synthesis that pooled it.
What it found
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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
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical challenges in the adaptation of AI predictive models in radiation oncology for gynaecological cancer: a systematic review by the radiation oncology-AI MITO group.La Radiologia medica · 2026Pooled it
- Ethical and Regulatory Challenges of Generative Artificial Intelligence in Healthcare: A Chinese Perspective.Journal of clinical nursing · 2026Article
- When Innovation Leaves People Behind: Reframing Accountability in Commercial Digital Health.Journal of medical Internet research · 2026Article
- MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis.IEEE transactions on medical imaging · 2026Article
- Attention guided fair artificial intelligence modeling for skin cancer diagnosis.NPJ digital medicine · 2026Article
- Training AI Models for Aesthetic Facial Evaluation: Focused Review and Framework to Mitigate Homogenizing Bias.Journal of medical Internet research · 2026Review
- Large Language Models Accurately Identify People Who Inject Drugs From Infectious Diseases Discharge Summaries in an Australian Hospital.Drug and alcohol review · 2026Article
- Review
- Integrating AI, imaging innovations and omics for precision medicine.BMJ open ophthalmology · 2026Article
- Predicting Anticipated Telehealth Use: Development of the CONTEST Score and Machine Learning Models Using a National U.S. Survey.Healthcare (Basel, Switzerland) · 2026Article
- Expectations and concerns of primary healthcare patients in rural areas and small towns in Poland regarding artificial intelligence.Scientific reports · 2026Article
- AI without representation is just inequity at scale: on the exportation of unrepresentative artificial intelligence models to the Global South.Frontiers in artificial intelligence · 2026Article
- Enhancing post-kidney transplant prognostication: an interpretable machine learning approach for longitudinal outcome prediction.NPJ digital medicine · 2025Article
- [Ethical considerations for artificial intelligence-enhanced brain-computer interface].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Evaluating the impact of data biases on algorithmic fairness and clinical utility of machine learning models for prolonged opioid use prediction.JAMIA open · 2025Article
- What Is Fair? Defining Fairness in Machine Learning for Health.Statistics in medicine · 2025Review
- FairFML: fair federated machine learning with a case study on reducing gender disparities in cardiac arrest outcome prediction.npj health systems · 2025Article
- Ethical considerations and robustness of artificial neural networks in medical image analysis under data corruption.Scientific reports · 2025Article
- Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.Briefings in bioinformatics · 2025Article
- A scoping review and evidence gap analysis of clinical AI fairness.NPJ digital medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the fairness of such data-driven insights remains a concern in high-stakes fields. Despite extensive developments, issues of AI fairness in clinical contexts have not been adequately addressed. A fair model is normally expected to perform equally across subgroups defined by sensitive variables (e.g., age, gender/sex, race/ethnicity, socio-economic status, etc.). Various fairness measurements have been developed to detect differences between subgroups as evidence of bias, and bias mitigation methods are designed to reduce the differences detected. This perspective of fairness, however, is misaligned with some key considerations in clinical contexts. The set of sensitive variables used in healthcare applications must be carefully examined for relevance and justified by clear clinical motivations. In addition, clinical AI fairness should closely investigate the ethical implications of fairness measurements (e.g., potential conflicts between group- and individual-level fairness) to select suitable and objective metrics. Generally defining AI fairness as "equality" is not necessarily reasonable in clinical settings, as differences may have clinical justifications and do not indicate biases. Instead, "equity" would be an appropriate objective of clinical AI fairness. Moreover, clinical feedback is essential to developing fair and well-performing AI models, and efforts should be made to actively involve clinicians in the process. The adaptation of AI fairness towards healthcare is not self-evident due to misalignments between technical developments and clinical considerations. Multidisciplinary collaboration between AI researchers, clinicians, and ethicists is necessary to bridge the gap and translate AI fairness into real-life benefits.
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.