ReviewJournal of the Korean Society of Radiology2025
AI for Lesion Detection in Musculoskeletal Radiology.
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.
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- Who Is the Surgeon Now: Human Hands or Machine Minds? Artificial Intelligence in Orthopedics from Diagnosis to Follow-Up-A Structured Narrative Review.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review provides an overview of the latest trends in lesion detection using AI in musculoskeletal imaging. It describes the types of deep learning networks used in detection AI and briefly explains their principles. Fracture-detection AI has shown improved sensitivity and reduced reporting time in multiple meta-analyses, and real-world validation in clinical settings has begun. Although many AIs have been developed to detect joint injuries and degenerative changes in MRI and CT/MRI detection models for bone metastasis and multiple myeloma, they have not yet reached a robust validation stage. Achieving clinical value requires attention to explainability, external validation and post-market monitoring, Picture Archiving Communicating System (PACS)-level integration, and legal and ethical issues and, therefore, proactive adoption by radiology professionals.
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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.