Artificial intelligence for the comprehensive approach to orphan/rare diseases: A scoping review

被引:0
|
作者
Ruge, L. M. Acero [1 ]
Lesmes, D. A. Vasquez [1 ]
Rincon, E. H. Hernandez [2 ]
Perez, L. P. Avella [3 ]
机构
[1] Univ La Sabana, Fac Med, Med Familiar & Comunitaria, Chia, Colombia
[2] Univ La Sabana, Fac Med, Dept Med Familiar & Salud Publ, Chia, Colombia
[3] Univ La Sabana, Fac Med, Chia, Colombia
来源
MEDICINA DE FAMILIA-SEMERGEN | 2025年 / 51卷 / 05期
关键词
Orphan diseases; Rare diseases; Artificial intelligence; Deep learning; Machine learning; Diagnosis; Computer assisted; Diagnostic imaging; Neuronal networks computer; RARE DISEASES;
D O I
10.1016/j.semerg.2024.102434
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Introduction: Orphan diseases (OD) are rare but collectively common, presenting challenges such as late diagnoses, disease progression, and limited therapeutic options. Recently, artificial intelligence (AI) has gained interest in the research of these diseases. Objective: To synthesize the available evidence on the use of AI in the comprehensive approach to orphan diseases. Methods: An exploratory systematic review of the Scoping Review type was conducted in PubMed, Bireme, and Scopus from 2019 to 2024. Results: fifty-six articles were identified, with 21.4% being experimental studies; 28 documents did not specify an OD, 8 documents focused primarily on genetic diseases; 53.57% focused on diagnosis, and 36 different algorithms were identified. Conclusions: The information found shows the development of AI algorithms in different clinical settings, confirming the potential benefits in diagnosis times, therapeutic options, and greater awareness among health professionals. (c) 2024 Sociedad Espanola de M & eacute;dicos de Atenci & oacute;n Primaria (SEMERGEN). Published by Elsevier Espa & ntilde;a, S.L.U. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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页数:25
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