ReviewJournal of the Korean Society of Radiology2025
Clinical Applications, Challenges & Pitfalls, and Recommendations for Large Language Model and Generative AI in Musculoskeletal Imaging.
Review in Journal of the Korean Society of Radiology, 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.
- Carpal Tunnel Syndrome Diagnosis: A Narrative Review of Complementary Roles of Neuromuscular Ultrasound and Electrodiagnostic Studies.Medical science monitor : international medical journal of experimental and clinical research · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative AI-including Generative Adversarial Networks, diffusion models, Large Language Models (LLMs), and more recently, vision-language models-is increasingly utilized in clinical practice for musculoskeletal imaging tasks such as disease diagnosis, image enhancement, image reconstruction, electronic health record summarization, and radiologic report generation. Integrating these technologies into radiology workflows can significantly advance radiology report generation, structured reporting, and patient-centered communication. However, challenges such as hallucination, bias, and performance drift remain persistent issues. Ensuring the safe and reliable use of LLMs in radiology requires domain-specific training, robust validation, and enhanced data privacy measures. This review summarizes available evidence regarding the potential utility of generative AI in musculoskeletal imaging and radiologic reporting, as well as the challenges and pitfalls in its application. Recommendations for future advancements and clinical translation are also discussed.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.