The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review.
artificial intelligence
machine learning
precision medicine
systemic sclerosis
Journal
Journal of personalized medicine
ISSN: 2075-4426
Titre abrégé: J Pers Med
Pays: Switzerland
ID NLM: 101602269
Informations de publication
Date de publication:
23 Jul 2022
23 Jul 2022
Historique:
received:
17
06
2022
revised:
18
07
2022
accepted:
19
07
2022
entrez:
27
7
2022
pubmed:
28
7
2022
medline:
28
7
2022
Statut:
epublish
Résumé
Systemic sclerosis (SSc) is a rare connective tissue disease that can affect different organs and has extremely heterogenous presentations. This complexity makes it difficult to perform an early diagnosis and a subsequent subclassification of the disease. This hinders a personalized approach in clinical practice. In this context, machine learning (ML), a branch of artificial intelligence (AI), is able to recognize relationships in data and predict outcomes. Here, we performed a narrative review concerning the application of ML in SSc to define the state of art and evaluate its role in a precision medicine context. Currently, ML has been used to stratify SSc patients and identify those at high risk of severe complications. Additionally, ML may be useful in the early detection of organ involvement. Furthermore, ML might have a role in target therapy approach and in predicting drug response. Available evidence about the utility of ML in SSc is sparse but promising. Future improvements in this field could result in a big step toward precision medicine. Further research is needed to define ML application in clinical practice.
Sections du résumé
BACKGROUND
BACKGROUND
Systemic sclerosis (SSc) is a rare connective tissue disease that can affect different organs and has extremely heterogenous presentations. This complexity makes it difficult to perform an early diagnosis and a subsequent subclassification of the disease. This hinders a personalized approach in clinical practice. In this context, machine learning (ML), a branch of artificial intelligence (AI), is able to recognize relationships in data and predict outcomes.
METHODS
METHODS
Here, we performed a narrative review concerning the application of ML in SSc to define the state of art and evaluate its role in a precision medicine context.
RESULTS
RESULTS
Currently, ML has been used to stratify SSc patients and identify those at high risk of severe complications. Additionally, ML may be useful in the early detection of organ involvement. Furthermore, ML might have a role in target therapy approach and in predicting drug response.
CONCLUSION
CONCLUSIONS
Available evidence about the utility of ML in SSc is sparse but promising. Future improvements in this field could result in a big step toward precision medicine. Further research is needed to define ML application in clinical practice.
Identifiants
pubmed: 35893293
pii: jpm12081198
doi: 10.3390/jpm12081198
pmc: PMC9331823
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
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