Análisis bibliométrico de la producción científica sobre estudios en malaria e inteligencia artificial 2000 – 2024

• Autor: Hernández López, Cecilia
• Formato: tesis de grado
• Fecha de Publicación: 2024
• Institución: Universidad Nacional De La Amazonía Peruana
• Repositorio: UNAPIquitos-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.unapiquitos.edu.pe:20.500.12737/10791
• Enlace del recurso: https://hdl.handle.net/20.500.12737/10791
• Nivel de acceso: acceso abierto
• Materia: Malaria; Inteligencia artificial; Publicación cientifica; Bibliometría; https://purl.org/pe-repo/ocde/ford#3.03.08

Introduction: Malaria is one of the most prevalent infectious diseases globally, especially in developing countries. In recent years, artificial intelligence (AI) has proven to be a promising tool in the research and management of diseases such as malaria. However, the scientific production on the intersection between malaria and AI requires a bibliometric analysis to understand its trends and development patterns. Objective: To describe the scientific production on Malaria and Artificial Intelligence studies in Scopus and Web of Science databases, from January 2000 to June 2024. Methods: A bibliometric study was conducted, using a cross-sectional and retrospective approach. Population: The population included all articles published in the Scopus and Web of Science databases within the specified period, resulting in a final sample of 525 articles after filtering for duplicates. Results: Scientific production in this field has shown significant growth since 2017, peaking in 2022 with 86 publications. The United States leads in terms of number of publications (383), followed by the United Kingdom (231) and India (218). The most productive institutions were the University of Oxford and the London School of Hygiene and Tropical Medicine. The most frequent keywords in the studies included “malaria,” “plasmodium falciparum,” and “machine learning,” highlighting the focus of artificial intelligence in malaria diagnosis and management. Conclusion: The bibliometric analysis demonstrates a growing integration of artificial intelligence in malaria studies, concentrated in countries with greater technological resources. This interdisciplinary approach presents opportunities for research expansion, especially in endemic regions.