APPLICATION OF ARTIFICIAL INTELLIGENCE IN DETECTING MUSCULOSKELETAL ABNORMALITIES THROUGH AUTOMATED RADIOGRAPHIC IMAGE ANALYSIS: SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Artificial intelligence (AI) is rapidly transforming diagnostic radiology, particularly in musculoskeletal imaging where precise and timely detection of abnormalities is essential for effective treatment. While numerous AI applications have been explored in individual studies, the lack of a consolidated synthesis of their diagnostic accuracy and clinical relevance in radiographic analysis of musculoskeletal disorders highlights a significant gap in the literature. Objective: This systematic review aims to evaluate the current evidence on the application of artificial intelligence in detecting musculoskeletal abnormalities using automated analysis of radiographic images, focusing on diagnostic accuracy, clinical utility, and limitations. Methods: A systematic review was conducted in accordance with PRISMA guidelines. Databases including PubMed, Scopus, Web of Science, and Cochrane Library were searched for studies published between 2018 and 2025. Eligible studies involved human subjects, applied AI to musculoskeletal radiographic imaging, and reported diagnostic outcomes. Data were extracted on study design, AI algorithms, sample size, outcomes, and performance metrics. Risk of bias was assessed using QUADAS-2 and Newcastle-Ottawa Scale tools based on study type. Results: Eight studies were included, encompassing diagnostic accuracy studies, narrative reviews, and one systematic review. AI models demonstrated high diagnostic performance, with AUC values ranging from 0.87 to >0.99, and strong correlation with expert radiologist interpretations. Applications included fracture detection, joint assessment, implant analysis, and TMJ osteoarthritis diagnosis. Variability in study designs and outcome reporting limited the feasibility of meta-analysis. Conclusion: AI demonstrates significant potential in improving the accuracy and efficiency of musculoskeletal radiographic interpretation. However, heterogeneity across studies and limited external validation underscore the need for further prospective, real-world research to support clinical integration and ensure generalizability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".