Evidence map›Paper›PMID 41083875›Full record

ArticleSurgical and radiologic anatomy : SRA2025

Role-based prompting to optimize chatgpt's anatomical assessment.

Ismail Sivri, Furkan Mehmet Ozden, Gamze Gul, Emre Kaygin, Tuncay Colak

Abstract readLetter
PubMed Publisher
In one paragraph

Article in Surgical and radiologic anatomy : SRA, 2025. 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.

Ismail SivriDepartment of Anatomy, Faculty of Medicine, Kocaeli University, Umuttepe Campus, 41380, İzmit, Kocaeli, Turkey. ismail.sivri@kocaeli.edu.tr.
Furkan Mehmet OzdenDepartment of Anatomy, Faculty of Medicine, Kocaeli University, Umuttepe Campus, 41380, İzmit, Kocaeli, Turkey.
Gamze GulDepartment of Anatomy, Faculty of Medicine, Kocaeli University, Umuttepe Campus, 41380, İzmit, Kocaeli, Turkey.
Emre KayginDepartment of Anatomy, Faculty of Medicine, Kocaeli University, Umuttepe Campus, 41380, İzmit, Kocaeli, Turkey.
Tuncay ColakDepartment of Anatomy, Faculty of Medicine, Kocaeli University, Umuttepe Campus, 41380, İzmit, Kocaeli, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent evaluations of ChatGPT’s performance in anatomy education have provided valuable insights into its capabilities and limitations. In response to the article by Singal and Goyal on the use of ChatGPT 3.5 and 4.0 for education on scalenovertebral triangle anatomy, we propose that the “neutral prompt” approach can be further optimized by using role-specific prompts. This approach, already utilized in other medical domains, allows large language models to generate more contextually appropriate and pedagogically useful outputs. We recommend considering the integration of role-specific prompts in future studies assessing AI-based tools in anatomy education, as a means to optimize their reliability and educational value.

Indexed as

AnatomyGenerative Artificial IntelligenceHumansLarge Language ModelsAnatomy educationChatGPTDear editorPrompt engineeringRole-based prompting

Identifiers

PMID41083875

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.