Evidence map›Paper›PMID 40851947›Full record

ArticleAnnals of medicine and surgery (2012)2025

Can generative AI-enabled ChatGPT or large language models (LLMs) assist orthopedic surgeons?

Chiranjib Chakraborty, Srijan Chatterjee, Manojit Bhattacharya, Md Aminul Islam

Abstract read
In one paragraph

Article in Annals of medicine and surgery (2012), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Chiranjib ChakrabortyDepartment of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, India.
Srijan ChatterjeeInstitute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si, Republic of Korea.
Manojit BhattacharyaDepartment of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, India.
Md Aminul IslamDepartment of Microbiology, COVID-19 Diagnostic Lab, Noakhali Science and Technology University, Noakhali, Bangladesh.ORCID https://orcid.org/0000-0003-1091-9726

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The incorporation of generative artificial intelligence (AI) models, like ChatGPT, into orthopedic surgery, has generated considerable interest among medical professionals. This paper provides an overview of the latest research on using ChatGPT in orthopedics. It explores the potential applications, advantages, obstacles, and ethical concerns of implementing this technology. Based on various studies, this paper delves into how ChatGPT contributes to patient communication, treatment planning, surgical outcomes, and diagnostic processes in orthopedic care. Although there is great potential for the technology to transform orthopedic practice, there are ongoing concerns about data security, ethical considerations, and the reliability of self-diagnosis tools. Effective collaboration among stakeholders, thorough validation procedures, and continuous research efforts are crucial in unlocking the complete potential of AI in orthopedic surgery while minimizing any risks involved. Despite the obstacles, the growing use of ChatGPT and other AI technologies in orthopedics indicates a significant change in healthcare delivery.

Indexed as

ChatGPTgenerative AIorthopedicspatient care

Identifiers

PMID40851947
PMCPMC12369820

What OpenQuestion holds

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LicenceCC BY-ND
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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.