期刊
JOURNAL OF PERSONALIZED MEDICINE
卷 12, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/jpm12081198
关键词
systemic sclerosis; machine learning; artificial intelligence; precision medicine
Machine learning shows promising applications in systemic sclerosis, including early diagnosis, classification, and treatment prediction, offering new possibilities for precision medicine.
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: 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: 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: 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.
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