Evidence map›Paper›PMID 42798676›Full record

ArticleDiscover artificial intelligence2026

Analyzing and redesigning deployment pathways for internally developed artificial intelligence tools in clinical education and health research.

Bo Xie, Ruoke Zhang

Abstract read
In one paragraph

Article in Discover artificial intelligence, 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

2 authors.

Bo XieSchool of Nursing, University of Texas at Austin, Austin, 78712 USA.ORCID 0000-0002-6016-6008
Ruoke ZhangSchool of Computing, University of Texas at Austin, Austin, 78712 USA.

Funding

StatWiseAI: An AI-Powered Educational Tool for Enhancing Methodological Rigor in Large-Scale Data AnalysisR25DA064339 · NIDA · UNIVERSITY OF TEXAS AT AUSTIN · PI Bo Xie · 2025 to 2026
$649k
NIDA NIH HHS R25 DA064339
6 · The paper itself

Abstract

Artificial intelligence (AI) tools for clinical education and health research are increasingly easy to imagine and prototype, but deploying internally developed tools into legitimate institutional use remains difficult. We conducted a retrospective comparative case study of four AI tools supported by the Nursing AI Studio in one academic health and social sciences environment. The four tools each was designed to address one of the following: clinical evaluation, tailored caregiver support, clinical simulation, and statistical assistance for biomedical and social-behavioral data. We operationalized deployment burden as the organizational, technical, and coordination work required to move a locally developed functioning AI-powered web application into legitimate institutional use. Across cases, early setup steps such as Amazon Web Services access, institutional subdomain configuration, and Information Security Office review were comparatively stable when institutional knowledge and reusable assets were available. The heavier burden occurred in first-team pathway discovery, enterprise integration, and cross-institutional access. The first case included approximately two-week gaps before later requests were initiated, reflecting time spent identifying the next institutional step rather than formal review time. Later internal cases reused such pathway knowledge, but recurring integration and changing compliance requirements persisted. The cross-institutional case introduced a distinct access problem that was not solved by the internal pathway. These findings suggest that deployment burden is not a single delay but a layered process involving discovery, reuse, recurrence, and context-specific bottlenecks. Shared studio-like support mechanisms may reduce repeated rediscovery and help health and social sciences teams plan more realistically for governance, infrastructure, collaboration, and sustainability.

Indexed as

Artificial intelligenceClinical informaticsDeployment pathwayEnterprise integrationFrontline innovation

Identifiers

PMID42798676
PMCPMC13612711

What Socratic 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 Socratic graph.