Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions.
arthritis
musculoskeletal system
rheumatic diseases
Journal
The Journal of rheumatology
ISSN: 0315-162X
Titre abrégé: J Rheumatol
Pays: Canada
ID NLM: 7501984
Informations de publication
Date de publication:
11 2022
11 2022
Historique:
accepted:
21
06
2022
pubmed:
16
7
2022
medline:
4
11
2022
entrez:
15
7
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 35840150
pii: jrheum.220326
doi: 10.3899/jrheum.220326
pmc: PMC9633365
mid: NIHMS1818025
doi:
Types de publication
Journal Article
Review
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1191-1200Subventions
Organisme : NIAMS NIH HHS
ID : P30 AR072580
Pays : United States
Organisme : NIAMS NIH HHS
ID : R21 AR074685
Pays : United States
Informations de copyright
Copyright © 2022 by the Journal of Rheumatology.
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