ArticleJournal of clinical orthopaedics and trauma2025
Generative artificial intelligence, large language models and ChatGPT in musculoskeletal Oncology: Current applications and future potential.
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.
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.
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.
Who cites it
2 citing papers in PubMed.
- Performance of multimodal large language models for the detection and characterization of bone lesions on radiographs.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Comparative Impact of ChatGPT and Conventional Search Tools on Clinical Reasoning Performance: A Randomized Crossover Study in Preclinical Medical Students.Advances in medical education and practice · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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What Socratic holds
Registered trials
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.