Artificial Intelligence as a Catalyst for Quality, Safety, and Value in Healthcare Delivery.

Philip R O Payne

Abstract read

In one paragraph

Article in Missouri medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Philip R O PayneDirector, Institute for Informatics, Data Science, and Biostatistics, the Janet and Bernard Becker Professor, Vice Chancellor for Biomedical Informatics and Data Science, WashU Medicine, and Chief Health AI Officer at BJC Health System and Washington University Medicine, St. Louis, Missouri, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has the potential to transform healthcare by enhancing quality, improving safety, and increasing value. However, achieving these benefits requires navigating a complex interplay between data quality, interoperability, computability, workflow integration, and governance, as well as the need to create and sustain what have been described as learning health systems. The full promise of AI will only be realized through rigorous data stewardship, the design and demonstration of novel computational methods, cross-disciplinary collaboration, and a commitment to responsible and equitable technology implementation. Substantial work remains to address these challenges and capitalize on these opportunities, which can and should lead to significant improvements in human health and well-being.

Indexed as

artificial intelligenceBiomedical informaticshealthcare policyhealthcare transformationqualitysafety

Identifiers

PMID41742928

PMCPMC12931601

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.