ReviewNorth Carolina medical journal2024
Toward an "Equitable" Assimilation of Artificial Intelligence and Machine Learning into Our Health Care System.
Review in North Carolina medical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies.European radiology · 2026Article
- A Multi-Modal Transfer Learning Framework to Reduce Health Disparities in Prostate Adenocarcinoma.bioRxiv : the preprint server for biology · 2025Article
- Computerized decision support for antimicrobial prescribing: what every antibiotic steward should know.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
- Artificial Intelligence in Health Care: Opportunities, Challenges, and the Road Ahead.North Carolina medical journal · 2024Article
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
Enthusiasm about the promise of artificial intelligence and machine learning in health care must be accompanied by oversight and remediation of any potential adverse effects on health equity goals that these technologies may create. We describe five equity imperatives for the use of AI/ML in health care that require attention from health care professionals, developers, and policymakers.
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.