Effectiveness of physician-based diagnosis versus diagnostic artificial intelligence algorithms in detecting communicable febrile diseases in Mexico

• Published in: Digital health Vol. 11; p. 20552076251353292
• Main Authors: Medina Fuentes, Enrique Alonso, Ruíz Valdez, Carmen Alicia, Hernández Bautista, Porfirio Felipe, Cabrera Gaytán, David Alejandro, Olivas Fabela, Guadalupe Minerva, Mireles Garza, José Alberto, Alejo Martínez, Olga María, Rocha Reyes, Brenda Leticia, Vallejos Parás, Alfonso, Arriaga Nieto, Lumumba, Pérez Andrade, Yadira, Jaimes Betancourt, Leticia, Valle Alvarado, Gabriel, Cruz Orozco, Oscar, Rivera Mahey, Mónica Grisel
• Format: Journal Article
• Language: English
• Published: United States SAGE Publications 01.01.2025; SAGE Publishing
• Subjects: Original; diagnostic algorithm; artificial neural network; Fever; artificial intelligence
• ISSN: 2055-2076; 2055-2076
• DOI: 10.1177/20552076251353292

Digital medicine is an important tool in the current healthcare landscape. Fever is an important reason for evaluating patients at first and second levels of care and a frequent symptom of diseases subject to epidemiological surveillance. To evaluate the diagnostic effectiveness of various algorithms in detecting communicable diseases of epidemiological interest in febrile patients at Hospital General Regional No. 1, Cd. Obregón, Sonora. An observational, descriptive, and retrospective study was conducted in a second-level hospital from 1 January 2022 to 31 December 2023, to determine Cohen's kappa and the sensitivity, specificity, positive and negative predictive values, precision and Youden's J index of diagnostic algorithms for 20 communicable diseases with respect to the doctors' diagnoses. Diagnostic algorithms were applied to the data of 909 cases. The sensitivities of Mediktor®, an artificial neural network-based algorithm, a medical diagnostic algorithm and a composite diagnostic algorithm were 11.97%, 64.09%, 69.92% and 99.37%, respectively, and the corresponding specificities were 93.43%, 91.24%, 27.01% and 5.11%, respectively. The neural network-based method yielded the highest Youden's J index. The medical diagnostic algorithm had the best sensitivity, whereas the specificity was greater for the two artificial intelligence algorithms.