Evidence map›Paper›PMID 40084802›Full record

ArticleJournal of advanced nursing2026

Investigation of Pressure Injuries With Visual ChatGPT Integration: A Descriptive Cross-Sectional Study.

Pelin Karaçay, Polat Goktas, Özgen Yaşar, Burak Uyanik, Sinem Uzlu, Kübra Coşkun, Mesut Benk

Abstract read
In one paragraph

Article in Journal of advanced nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

7 authors.

Pelin KaraçaySchool of Nursing, Koç University, Istanbul, Türkiye.ORCID https://orcid.org/0000-0002-5627-2836
Polat GoktasUCD School of Computer Science, University College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0001-7183-6890
Özgen YaşarGraduade School of Health Sciences, Koç University, Istanbul, Türkiye.ORCID https://orcid.org/0009-0002-3720-4878
Burak UyanikKoç University Hospital, Istanbul, Türkiye.ORCID https://orcid.org/0009-0002-2430-6509
Sinem UzluKoç University Hospital, Istanbul, Türkiye.ORCID https://orcid.org/0009-0007-6825-5877
Kübra CoşkunIndepended Researcher, Istanbul, Türkiye.ORCID https://orcid.org/0009-0002-6040-2936
Mesut BenkAmerikan Hospital, Istanbul, Türkiye.ORCID https://orcid.org/0009-0000-9818-8870

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimThis study aimed to assess the performance of Visual ChatGPT in staging pressure injuries using real patient images, compare it to manual staging by expert nurses, and evaluate its applicability as a supportive tool in wound care management.

designThis study used a descriptive and comparative cross-sectional design.

methodsThe study analysed 155 patient pressure injury images from a hospital database, staged by expert nurses and Visual ChatGPT using the National Pressure Injury Advisory Panel guidelines. Visual ChatGPT's performance was tested in two scenarios: with images only and with images plus wound characteristics. Diagnostic performance was evaluated, including sensitivity, specificity, accuracy, and inter-rater agreement (Kappa).

resultsExpert nurses demonstrated superior accuracy and specificity across most pressure injury stages. Visual ChatGPT performed comparably in early-stage pressure injuries, especially when wound characteristics were included, but struggled with unstageable and deep-tissue pressure injuries.

conclusionVisual ChatGPT shows potential as an artificial intelligence tool for pressure injury staging and wound management in nursing. However, improvements are necessary for complex cases, ensuring that artificial intelligence complements clinical judgement. IMPLICATIONS FOR PROFESSION AND/OR PATIENT CARE: Visual ChatGPT can serve as an innovative artificial intelligence tool in clinical settings, assisting less experienced nurses and those in areas with limited wound care specialists in staging and managing pressure injuries. REPORTING

methodThe STROBE checklist was followed for reporting cross-sectional studies in line with the relevant EQUATOR guidelines. PATIENT CONTRIBUTION: No patient or public contribution.

Indexed as

Artificial IntelligencePressure UlcerAdultAgedCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansMaleMiddle Agedartificial intelligenceChatGPTclinical decision supporthealthcare technologynursing practicepressure injurieswound care management

Identifiers

PMID40084802
PMCPMC12721935

What Socratic holds

Textmetadata
LicenceCC BY-NC
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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 Socratic graph.