Evidence map›Paper›PMID 40838089›Full record

ArticleJournal of clinical orthopaedics and trauma2025

Generative artificial intelligence, large language models and ChatGPT in musculoskeletal Oncology: Current applications and future potential.

Tomas Zamora, Paulina Salas, Sebastian Zuñiga, Eduardo Botello, Marcelo E Andia

Abstract read
In one paragraph

Article in Journal of clinical orthopaedics and trauma, 2025. 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

5 authors.

Tomas ZamoraDepartment of Orthopaedic Surgery. Pontificia Universidad Catolica de Chile, Santiago, Chile.
Paulina SalasDepartment of Orthopaedic Surgery. Pontificia Universidad Catolica de Chile, Santiago, Chile.
Sebastian ZuñigaDepartment of Orthopaedic Surgery. Pontificia Universidad Catolica de Chile, Santiago, Chile.
Eduardo BotelloDepartment of Orthopaedic Surgery. Pontificia Universidad Catolica de Chile, Santiago, Chile.
Marcelo E Andiai-HEALTH Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative artificial intelligence (AI), particularly large language models (LLMs), has emerged as a transformative technology across all medical specialties, including musculoskeletal (MSK) oncology. These models, such as ChatGPT and others, can process natural language, synthesize vast amounts of information, and generate contextually relevant outputs that resemble human communication. In orthopedic oncology, LLMs show promise in facilitating literature reviews, enhancing patient education, and supporting clinical decision-making by analyzing multidimensional data while providing improved logic-based reasoning. Additionally, they can assist in radiological and pathological workflows by interpreting imaging reports and drafting diagnostic summaries, thereby increasing efficiency and accuracy. In the near future, they are expected to aid in real-time patient follow-up and counseling, information transfer, efficient diagnostics, and even continuous surgical education and assistance. Despite their potential, challenges such as the risk of inaccuracies and biases, as well as the necessity for continuous supervision, warrant a cautious and responsible integration into clinical practice. This narrative review examines the current applications of LLMs in MSK oncology, their limitations, and their future potential in shaping precision medicine and equitable healthcare delivery.

Indexed as

Artificial intelligenceClinicalDecision support systemsMachine learningMedical informaticsMusculoskeletal neoplasmsNatural language processing

Identifiers

PMID40838089
PMCPMC12361804

What Socratic 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 Socratic graph.