ArticleLearning health systems2026
Data Fit for Health Equity: Learning Health Systems, AI, and the STANDING Together Recommendations.
Article in Learning health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).Journal of the National Medical Association · 2026Review
- Data Fit for Health Equity: Learning Health Systems, AI, and the STANDING Together Recommendations.Learning health systems · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Artificial Intelligence (AI) tools may deliver significant improvements in healthcare and Learning Health Systems are well positioned to benefit. However, during the adoption of AI, Learning Health Systems should consider the potential for AI to exacerbate health inequity and perpetuate biases that exist in healthcare and its associated data. Methods: The STANDING Together recommendations provide a method to identify and report potential bias during the curation of datasets for AI and the development of AI from those datasets. The recommendations could form a key learning cycle within a Learning Health System ensuring transparent reporting of healthcare data use and the implementation of AI healthcare technologies that promote health equity. Results and Conclusions: Learning Health Systems are well placed to adopt the STANDING Together best practice recommendations for using healthcare data as they are likely to have both the capabilities to implement the recommendations and the strategic goals that will realize the value of health data and AI that promotes health equity. The STANDING Together recommendations are available from www.datadiversity.org.
Indexed as
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.