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Utilizing Artificial Intelligence Among Patients With Diabetes: A Systematic Review and Meta-Analysis.
Alhalafi, Abdullah; Alqahtani, Saif M; Alqarni, Naif A; Aljuaid, Amal T; Aljaber, Ghade T; Alshahrani, Lama M; Mushait, Hadeel; Nandi, Partha A.
Afiliación
  • Alhalafi A; Department of Family and Community Medicine, University of Bisha, Bisha, SAU.
  • Alqahtani SM; College of Medicine, University of Bisha, Bisha, SAU.
  • Alqarni NA; College of Medicine, University of Bisha, Bisha, SAU.
  • Aljuaid AT; College of Medicine, University of Bisha, Bisha, SAU.
  • Aljaber GT; Department of Medicine, Batterjee Medical College, Aseer, SAU.
  • Alshahrani LM; College of Medicine, King Khalid University, Abha, SAU.
  • Mushait H; College of Medicine, King Khalid University, Abha, SAU.
  • Nandi PA; Department of Family and Community Medicine, University of Bisha, Bisha, SAU.
Cureus ; 16(4): e58713, 2024 Apr.
Article en En | MEDLINE | ID: mdl-38779284
ABSTRACT
Diabetes mellitus, a condition characterized by dysregulation of blood glucose levels, poses significant health challenges globally. This meta-analysis and systematic review aimed to evaluate the effectiveness of artificial intelligence (AI) in managing diabetes, underpinned by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review scrutinized articles published between January 2019 and February 2024, sourced from six electronic databases Web of Science, Google Scholar, PubMed, Cochrane Library, EMBASE, and MEDLINE, using keywords such as "Artificial intelligence use in medicine, Diabetes management, Health technology, Machine learning, Diabetic patients, AI applications, and Health informatics." The analysis revealed a notable variance in the prevalence of diabetes symptoms between patients managed with AI models and those receiving standard treatments or other machine learning models, with a risk ratio (RR) of 0.98 (95% CI 0.88-1.08, I2 = 0%). Sub-group analyses, focusing on symptom detection and management, consistently showed outcomes favoring AI interventions, with RRs of 0.97 (95% CI 0.87-1.08, I2 = 0%) for symptom detection and 0.97 (95% CI 0.56-1.57, I2 = 0%) for management, respectively. The findings underscore the potential of AI in enhancing diabetes care, particularly in early disease detection and personalized lifestyle recommendations, addressing the significant health risks associated with diabetes, including increased morbidity and mortality. This study highlights the promising role of AI in revolutionizing diabetes management, advocating for its expanded use in healthcare settings to improve patient outcomes and optimize treatment efficacy.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Cureus Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Cureus Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos