Evidence map›Paper›PMID 42769379›Full record

ArticleHealth affairs scholar2026

When nurses are held accountable for AI-driven clinical decisions, they must be engaged early and meaningfully.

Meghan Reading Turchioe, Ann Wieben, Carolyn Sun, Christina Baker, Benjamin J Galatzan

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

5 authors.

Meghan Reading TurchioeColumbia University School of Nursing, New York, NY 10032, USA.ORCID https://orcid.org/0000-0002-6264-6320
Ann WiebenUniversity of Wisconsin-Madison, School of Nursing, Madison, WI 53706, USA.ORCID https://orcid.org/0000-0001-9275-0439
Carolyn SunHunter College, School of Nursing, New York City, NY 10010, USA.ORCID https://orcid.org/0000-0001-9628-6901
Christina BakerUniversity of Colorado, Anschutz, College of Nursing, Aurora, CO 80045, USA.ORCID https://orcid.org/0000-0002-6391-0314
Benjamin J GalatzanUniversity of Alabama, Capstone College of Nursing, Tuscaloosa, AL 35487, USA.ORCID https://orcid.org/0000-0002-0220-8388

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Health systems are adopting AI-powered tools faster than policy can address the implications for clinician liability. Nurses, as the largest healthcare profession and the most proximal clinicians to patients, must decide whether to accept, modify, or override AI-derived recommendations, often while managing competing cognitive demands. Yet the question of who bears responsibility when AI contributes to patient harm remains unresolved in policy and case law. At the 2025 American Medical Informatics Association Annual Symposium, a structured debate examined whether nurses should be held liable for AI-assisted clinical decisions. Proponents argued that professional ethics, existing tort precedent, and the nurse's role as human-in-the-loop require accountability. Opponents countered that AI opacity, unfair liability distribution, and limited nurse agency in AI procurement preclude fair responsibility assignment. Two reforms are essential: meaningful nurse engagement throughout the AI lifecycle and nursing-specific considerations in AI legislation and regulatory frameworks.

Indexed as

AI governanceartificial intelligenceclinical decision supporthealth policyhuman-in-the-loopliabilitymalpractisenursingnursing informaticspatient safety

Identifiers

PMID42769379
PMCPMC13591276

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.