ArticleNursing outlook
Building competency in artificial intelligence and bias mitigation for nurse scientists and aligned health researchers.
Article in Nursing outlook. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reimagining nursing practice in the era of AI: a qualitative systematic review and meta-synthesis of nurses' lived experiences.Journal of health, population, and nutrition · 2026Pooled it
- Technology Review: The Hidden Influence of AI in Orthopaedic Surgery.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026Review
- A narrower access gap, yet persistent divides: the digital inequality among China's urban-rural older adults.BMC public health · 2026Article
- Ethical considerations of AI integration in palliative care: a qualitative study.BMC palliative care · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Healthcare systems are increasingly integrating artificial intelligence and machine learning (AI/ML) tools into patient care, potentially influencing clinical decisions for millions. However, concerns are growing about these tools reinforcing systemic inequities. To address bias in AI/ML tools and promote equitable outcomes, guidelines for mitigating this bias and comprehensive workforce training programs are necessary. In response, we developed the multifaceted Human-Centered Use of Multidisciplinary AI for Next-Gen Education and Research (HUMAINE), informed by a comprehensive scoping review, training workshops, and a research symposium. The curriculum, which focuses on structural inequities in algorithms that contribute to health disparities, is designed to equip scientists with AI/ML competencies that allow them to effectively address these structural inequities and promote health equity. The curriculum incorporates the perspectives of clinicians, biostatisticians, engineers, and policymakers to harness AI's transformative potential, with the goal of building an inclusive ecosystem where cutting-edge technology and ethical AI governance converge to create a more equitable healthcare future for all.
Indexed as
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.