Impact of AI on Disease Prediction and Early Detection: A Systematic Review

Authors

  • Carlos Zepeda Lugo Autonomous University of Baja California image/svg+xml
  • Andrea María Insfran Rivarola Laboratorio de Producción y Métodos del Departamento de Ingeniería Industrial
  • Ana Arévalos Laboratorio de Producción y Métodos del Departamento de Ingeniería Industrial, Facultad de Ingeniería, Universidad Nacional de Asunción, Paraguay

DOI:

https://doi.org/10.69681/lajae.v7i1.35

Keywords:

Machine learning, healthcare 4.0, medical diagnosis, predictive algorithms

Abstract

Artificial Intelligence (IA) has emerged as a transformative tool in the healthcare environment. This review aims to evaluate the effects of AI-based technologies on the early prediction and detection of diseases. A systematic review of the literature was conducted following the PRISMA methodology guidelines. Out of 27,359 studies, 28 were included in the analysis as they provided evidence supporting the effectiveness of machine learning algorithms. The findings indicated that the algorithms were predominantly utilized for the prediction of cardiovascular (n = 9), neurological (n = 6), oncological (n = 5), hepatic (n = 3), pulmonary (n = 3), and infectious diseases (n = 2). In addition, the accuracy of the main algorithms was measured in 23 studies, and 19 reported values greater than 90%. The application of AI techniques has demonstrated significant potential to improve the diagnosis and prediction of a wide range of diseases.

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Author Biographies

Carlos Zepeda Lugo, Autonomous University of Baja California

Received the B.S. degree in bioengineer from the Autonomous University of Baja California (UABC), Mexico in 2012. In 2016 and 2020 he obtained the M.S. degree in Industrial Engineering and the Doctor in Science degree from UABC respectively. He is currently a professor of industrial engineering in the School of Engineering, Architecture and Design at the Autonomous University of Baja California, Mexico. He is in charge of the of Biomedical Engineering area of the Institute of Health Services of the State of Baja California. His research focuses on Lean-Six Sigma in healthcare, clinical engineering and healthcare 4.0. Dr. Zepeda has participated in a few research projects related to healthcare process improvement.

Andrea María Insfran Rivarola, Laboratorio de Producción y Métodos del Departamento de Ingeniería Industrial

Doctora en Ciencias de la Ingeniería Industrial por la Universidad Autónoma de Baja California, México. Es docente investigadora del Departamento de Ingeniería Industrial y Jefa del Laboratorio de Producción y Métodos de la FIUNA. Sus líneas de investigación se centran principalmente en sistemas de gestión y mejora continua mediante Lean Six Sigma e inocuidad alimentaria. Además, se encuentra categorizada en el SISNI de Conacyt Paraguay en el nivel I. Es docente de las cátedras Lean Production y Análisis, Medición y Mejora de Procesos.

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Published

2024-12-31

How to Cite

Zepeda Lugo, C. A., Insfran Rivarola, A. M., & Arévalos Ferreira, A. P. (2024). Impact of AI on Disease Prediction and Early Detection: A Systematic Review. Latin American Journal of Applied Engineeringg, 7(1), 25–35. https://doi.org/10.69681/lajae.v7i1.35

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Vol. 7 No. 1 (2024)

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Literature review

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Copyright (c) 2024 Carlos Zepeda Lugo, Andrea Insfran-Rivarola, Ana Arévalos

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