The Role of Artificial Intelligence in Treating Musculoskeletal Disorders
Bibliographic record
Abstract
Musculoskeletal disorders (MSDs) are a group of conditions affecting the locomotor system; the symptoms can range from fractures or sprains to ongoing pain and disability. According to the World Health Organization, MSD conditions are a leading cause of disability worldwide. In the United States alone, 1 in every 2 adults live with a musculoskeletal condition. The most common of these conditions include osteoarthritis, back and neck pain, as well as inflammatory conditions such as rheumatoid arthritis. The prevalence of these conditions results in a limitation on daily functioning of both children and working adults. In 2011, musculoskeletal conditions cost approximately US$213 billion in healthcare costs, accompanied by an overall reduction in workplace productivity. In addition, a recent study reported that MSDs were the highest contributor to global disability in 2017. To manage the rising levels of musculoskeletal conditions, artificial intelligence (AI) is being increasingly used in healthcare settings and has shown potential in prognosing and determining the severity of several MSDs. The following review paper will examine AI modalities present in diagnosing and/or treating MSDs and future implications in helping treat those individuals with these conditions. Artificial intelligence has been used in the healthcare through surgical and imaging interventions, in addition to helping diagnose and better treat, one of the most prevalent MSDs, arthritis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".