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The Use of Artificial Intelligence for Skin Disease Diagnosis in Primary Care Settings: A Systematic Review.
Escalé-Besa, Anna; Vidal-Alaball, Josep; Miró Catalina, Queralt; Gracia, Victor Hugo Garcia; Marin-Gomez, Francesc X; Fuster-Casanovas, Aïna.
Afiliação
  • Escalé-Besa A; Centre d'Atenció Primària Navàs-Balsareny, Institut Català de la Salut, 08670 Navàs, Spain.
  • Vidal-Alaball J; Health Promotion in Rural Areas Research Group, Gerència d'Atenció Primària i a la Comunitat de la Catalunya Central, Institut Català de la Salut, 08242 Manresa, Spain.
  • Miró Catalina Q; Faculty of Medicine, University of Vic-Central University of Catalonia, 08500 Vic, Spain.
  • Gracia VHG; Health Promotion in Rural Areas Research Group, Gerència d'Atenció Primària i a la Comunitat de la Catalunya Central, Institut Català de la Salut, 08242 Manresa, Spain.
  • Marin-Gomez FX; Faculty of Medicine, University of Vic-Central University of Catalonia, 08500 Vic, Spain.
  • Fuster-Casanovas A; Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, 082424 Manresa, Spain.
Healthcare (Basel) ; 12(12)2024 Jun 13.
Article em En | MEDLINE | ID: mdl-38921305
ABSTRACT
The prevalence of dermatological conditions in primary care, coupled with challenges such as dermatologist shortages and rising consultation costs, highlights the need for innovative solutions. Artificial intelligence (AI) holds promise for improving the diagnostic analysis of skin lesion images, potentially enhancing patient care in primary settings. This systematic review following PRISMA guidelines examined primary studies (2012-2022) assessing AI algorithms' diagnostic accuracy for skin diseases in primary care. Studies were screened for eligibility based on their availability in the English language and exclusion criteria, with risk of bias evaluated using QUADAS-2. PubMed, Scopus, and Web of Science were searched. Fifteen studies (2019-2022), primarily from Europe and the USA, focusing on diagnostic accuracy were included. Sensitivity ranged from 58% to 96.1%, with accuracies varying from 0.41 to 0.93. AI applications encompassed triage and diagnostic support across diverse skin conditions in primary care settings, involving both patients and primary care professionals. While AI demonstrates potential for enhancing the accuracy of skin disease diagnostics in primary care, further research is imperative to address study heterogeneity and ensure algorithm reliability across diverse populations. Future investigations should prioritise robust dataset development and consider representative patient samples. Overall, AI may improve dermatological diagnosis in primary care, but careful consideration of algorithm limitations and implementation strategies is required.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Healthcare (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Espanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Healthcare (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Espanha