Evidence map›Paper›PMID 42490541›Full record

ArticleJMIR formative research2026

AI Software Among Commercially Insured Populations: Cross-Sectional Study of Patient and Plan Characteristics.

Elsa Zhang, Yujia Jin, Ching-Ching Claire Lin, Raymond Kuo, Joshua M Liao

Abstract read
In one paragraph

Article in JMIR formative research, 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

5 authors.

Elsa ZhangProgram on Policy Evaluation and Learning, Dallas, TX, United States.ORCID 0009-0005-3499-9531
Yujia JinDepartment of Internal Medicine, Division of General Internal Medicine, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, United States, 1 214 648 3111.ORCID 0009-0002-1889-5378
Ching-Ching Claire LinInstitute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan.ORCID 0000-0002-4882-2826
Raymond KuoInstitute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan.ORCID 0000-0003-4980-853X
Joshua M LiaoProgram on Policy Evaluation and Learning, Dallas, TX, United States.ORCID 0000-0002-9788-9074

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: We describe the adoption of AI software among commercially insured adults from 2018 to 2023 and find rapid growth but variable use across patients, plans, and regional characteristics, highlighting the need for research and policy to support equitable and beneficial adoption.

Indexed as

Artificial IntelligenceInsurance, HealthSoftwareAdultCross-Sectional StudiesHumansUnited Statesdigital innovationhealth policyMedicareradiologysoftware-as-a-service

Identifiers

PMID42490541
PMCPMC13394851

What OpenQuestion holds

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LicenceCC BY
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.