ReviewSkeletal radiology2022
AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?
Review in Skeletal radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled 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.
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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review of artificial intelligence development and evaluation for MRI diagnosis of knee ligament or meniscus tears.Skeletal radiology · 2024Pooled it
- Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review.RMD open · 2023Pooled it
- Clinical Applications, Challenges & Pitfalls, and Recommendations for Large Language Model and Generative AI in Musculoskeletal Imaging.Journal of the Korean Society of Radiology · 2025Review
- [Preface to the Special Issue on Advancing Musculoskeletal Imaging: Cutting-Edge AI Innovations].Journal of the Korean Society of Radiology · 2025Article
- Review
- Article
- Application of artificial intelligence in osteoporosis: a review.Frontiers in medicine · 2025Review
- ChatGPT's diagnostic performance based on textual vs. visual information compared to radiologists' diagnostic performance in musculoskeletal radiology.European radiology · 2025Article
- Evaluating the Performance and Bias of Natural Language Processing Tools in Labeling Chest Radiograph Reports.Radiology · 2024Article
- Reading Times of Common Musculoskeletal MRI Examinations: A Survey Study.Tomography (Ann Arbor, Mich.) · 2024Article
- AI applications in musculoskeletal imaging: a narrative review.European radiology experimental · 2024Review
- How AI May Transform Musculoskeletal Imaging.Radiology · 2024Review
- The Role and Efficiency of an AI-Powered Software in the Evaluation of Lower Limb Radiographs before and after Total Knee Arthroplasty.Journal of clinical medicine · 2023Article
- Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- Musculoskeletal MR Image Segmentation with Artificial Intelligence.Advances in clinical radiology · 2022Article
- Latest advancements in imaging techniques in OA.Therapeutic advances in musculoskeletal disease · 2022Review
- Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance.Applied bionics and biomechanics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Artificial intelligence (AI) is expected to bring greater efficiency in radiology by performing tasks that would otherwise require human intelligence, also at a much faster rate than human performance. In recent years, milestone deep learning models with unprecedented low error rates and high computational efficiency have shown remarkable performance for lesion detection, classification, and segmentation tasks. However, the growing field of AI has significant implications for radiology that are not limited to visual tasks. These are essential applications for optimizing imaging workflows and improving noninterpretive tasks. This article offers an overview of the recent literature on AI, focusing on the musculoskeletal imaging chain, including initial patient scheduling, optimized protocoling, magnetic resonance imaging reconstruction, image enhancement, medical image-to-image translation, and AI-aided image interpretation. The substantial developments of advanced algorithms, the emergence of massive quantities of medical data, and the interest of researchers and clinicians reveal the potential for the growing applications of AI to augment the day-to-day efficiency of musculoskeletal radiologists.
Indexed as
Identifiers
34341865What 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.