Evidence map›Paper›PMID 41872690›Full record

ArticleClinical anatomy (New York, N.Y.)2026

Accuracy of ChatGPT-4o in Identifying Anatomical Structures on Cadaveric Images: A Practical Anatomy Examination Study.

Piotr Melczewski, Monika Chowaniec, Wiktoria Larysz, Anna Pioskowik, Michał Mordarski, Kacper Bąk, Grzegorz Fibiger, Kamil Możdżeń, Jerzy Walocha, Tomasz Kozioł

Abstract read
In one paragraph

Article in Clinical anatomy (New York, N.Y.), 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. Review
  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

10 authors.

Piotr MelczewskiJagiellonian University Medical College, Kraków, Poland.
Monika ChowaniecJagiellonian University Medical College, Kraków, Poland.
Wiktoria LaryszJagiellonian University Medical College, Kraków, Poland.
Anna PioskowikJagiellonian University Medical College, Kraków, Poland.
Michał MordarskiJagiellonian University Medical College, Kraków, Poland.ORCID https://orcid.org/0009-0000-5885-8286
Kacper BąkJagiellonian University Medical College, Kraków, Poland.ORCID https://orcid.org/0009-0003-7570-0378
Grzegorz FibigerDepartment of Anatomy, Jagiellonian University Medical College, Kraków, Poland.ORCID https://orcid.org/0009-0007-6109-453X
Kamil MożdżeńDepartment of Anatomy, Jagiellonian University Medical College, Kraków, Poland.
Jerzy WalochaDepartment of Anatomy, Jagiellonian University Medical College, Kraków, Poland.ORCID https://orcid.org/0000-0001-8587-2315
Tomasz KoziołDepartment of Anatomy, Jagiellonian University Medical College, Kraków, Poland.ORCID https://orcid.org/0009-0009-7207-9532

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid expansion of large language models (LLMs), including ChatGPT, has generated interest in their potential role in medical education. Although prior studies have evaluated LLM performance in theoretical assessments and selected imaging tasks, their ability to recognize anatomical structures in a cadaver-based practical examination setting remains unclear. This study assessed the accuracy of ChatGPT-4o in identifying anatomical structures on photographs of cadaveric specimens marked in the same manner as during practical anatomy examinations. A total of 265 anatomical structures were labeled on cadaveric specimens from the Department of Anatomy of the Jagiellonian University Medical College and photographed. Using a standardized prompt, the free version of ChatGPT-4o was asked to identify each marked structure, with up to three attempts permitted and standardized feedback provided after incorrect responses. Identification was considered correct only when a valid anatomical term precisely corresponding to the indicated structure was provided. The overall accuracy was 22.26%. Correct identification occurred on the first attempt in 33 cases, on the second in 15, and on the third in 11. Accuracy was highest for osteological structures (64.71% correct within three attempts) and lowest for isolated thoracic organs (8.82%). The model frequently misidentified anatomical regions and occasionally generated non-existent anatomical terms. At its current stage of development, ChatGPT-4o does not appear to be a reliable tool for cadaver-based anatomical structure recognition or practical anatomy examination support.

Indexed as

AnatomyCadaverGenerative Artificial IntelligenceHumansLarge Language Modelsanatomy educationartificial intelligencecadaveric anatomyChatGPTmedical education

Identifiers

PMID41872690
PMCPMC13472283

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Registered trials

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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.