Evidence map›Paper›PMID 41281039›Full record

ArticleCureus2025

Potentials of ChatGPT in Anatomy Research: A Conversation With ChatGPT.

Sarika R Tigga, Sandeep Saluja

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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.

Sarika R TiggaAnatomy, University College of Medical Sciences, Guru Teg Bahadur Hospital, New Delhi, IND.
Sandeep SalujaAnatomy, Amrita School of Medicine, Faridabad, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe field of natural language processing (NLP) has experienced considerable growth in recent times, advancing towards the capability of human-like language production. Among these developments, ChatGPT (OpenAI, San Francisco, CA, USA) is an excellent language model efficient in producing rational and contextually pertinent text responses. Its capability to comprehend and react to user inputs has created new opportunities for a wide range of implementations, encompassing research and publication. This study intended to evaluate the capabilities of ChatGPT and its optimal utilisation in anatomy research (AR). METHODOLOGY: The study included 22 prompts, which were submitted to ChatGPT after obtaining an online subscription to the Plus plan. These prompts were formulated after consensus among the investigators. The chatbot's responses were documented and assessed with regard to relevance, precision, and reliability.

resultsChatGPT was observed to be helpful for the investigators to discover the potential topics for exploration in anatomy and recognise research gaps in existing literature. The chatbot created a good synopsis of an article and wrote a satisfactory literature review and research proposal on the presented topic. It was noted to provide adequate assistance to the researchers in the preparation of the manuscript and was quite capable of suggesting the applicable statistical test for any given data. The chatbot also performed various statistical tests and provided assistance in the graphical visualisation of the data by drawing histograms, box plots, and scatter plots.

conclusionsChatGPT can be a valuable interactive tool for the investigators with the capacity to play a fundamental role in AR if utilised methodically. It provides considerable assistance to the anatomy researchers during the selection of research topics, writing of research proposals, preparation of the manuscript, and carrying out of statistical analysis.

Indexed as

anatomyanatomy researchartificial intelligencechatbotchatgpt

Identifiers

PMID41281039
PMCPMC12631707

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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.