Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions.
Amanda E NelsonLiubov ArbeevaPublished in: The Journal of rheumatology (2022)
There has been rapid growth in the use of artificial intelligence (AI) analytics in medicine in recent years, including in rheumatic and musculoskeletal diseases (RMDs). Such methods represent a challenge to clinicians, patients, and researchers, given the "black box" nature of most algorithms, the unfamiliarity of the terms, and the lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this subject area in a way that is relevant and meaningful to clinicians and researchers. We hope to provide some insights into relevant strengths and limitations, reporting guidelines, as well as recent examples of such analyses in key areas, with a focus on lessons learned and future directions in diagnosis, phenotyping, prognosis, and precision medicine in RMDs.
Keyphrases
- artificial intelligence
- machine learning
- big data
- deep learning
- palliative care
- end stage renal disease
- rheumatoid arthritis
- newly diagnosed
- current status
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- emergency department
- transcription factor
- high throughput
- clinical practice
- prognostic factors
- adverse drug
- public health
- risk assessment
- sensitive detection
- loop mediated isothermal amplification