Evidence map›Paper›PMID 41509064›Full record

ArticleHealth affairs scholar2026

Toward an operational definition of Artificial Intelligence for health care informatics: a Delphi survey.

Carolyn Sun, Shakib Hossain, Shannon L Harris

Abstract read
In one paragraph

Article in Health affairs scholar, 2026. 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

3 authors.

Carolyn SunHunter-Bellevue School of Nursing, Hunter College, New York, NY 10010, United States.ORCID https://orcid.org/0000-0001-9628-6901
Shakib HossainNewYork-Presbyterian Columbia Irving Medical Center, New York, NY 10032, United States.ORCID https://orcid.org/0009-0004-5280-9132
Shannon L HarrisVirginia Commonwealth University, Richmond, VA 23284, United States.ORCID https://orcid.org/0000-0002-9877-165X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The proliferation of Artificial Intelligence (AI) technologies, fueled by advancements in computational power and generative models, is rapidly reshaping healthcare delivery and research. However, the absence of a standardized definition of AI impedes regulatory development, confounds public discourse, and hinders clinical adoption. This study provides clarity for AI developers and users in terminology surrounding the topic, which will ultimately assist in mitigating risks to patients and the public. Utilizing a multiphase Delphi method involving international informatics experts, we synthesized existing definitions and facilitated consensus on an operational definition of AI tailored to healthcare contexts. Our findings aim to establish a foundational framework to guide ethical governance, promote funding alignment, and optimize AI integration in clinical settings.

Indexed as

Artificial IntelligenceconsensusdefinitionDelphi survey

Identifiers

PMID41509064
PMCPMC12778320

What OpenQuestion holds

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LicenceCC BY
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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.