ArticleAnnals of family medicine2025
Data Transformation to Advance AI/ML Research and Implementation in Primary Care.
Article in Annals of family medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issuedCorrection.2026
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence and machine learning (AI/ML) in health care is accelerating at a breathtaking pace. As the largest health care delivery platform, primary care is where the power, opportunity, and future of AI/ML are most likely to be realized in the broadest and most ambitious scale. However, there is a relative lack of organized, open, large-scale primary care datasets to attract industry and academia in primary care-focused research and development. This article proposes a set of high-level considerations around the data transformation that is needed to enable the growth of AI/ML applications in primary care. These considerations call for automation of data collection, organization of fragmented data, identification of primary care-specific use cases, integration of AI/ML into human workflows, and surveillance for unintended consequences. By unlocking the power of its data, primary care can play a leading role in advancing health care AI/ML to support patients, clinicians, and the health of the nation.
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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.