Expert systems for medical decision support: the case of Ebola in the Democratic Republic of the Congo (DRC) – The impact of artificial intelligence on the quality of healthcare

Abstract

The recurrence of Ebola virus disease outbreaks in the Democratic Republic of the Congo poses a major challenge to the healthcare system due to the disease’s high fatality rate, rapid spread and the difficulties involved in early diagnosis, isolation of suspected cases and clinical management. These health crises highlight the limitations of traditional surveillance methods, which are often hampered by poor-quality data, a lack of reliable data, weak coordination of interventions and inadequate digital infrastructure.

In this context, artificial intelligence and expert systems for medical decision support are emerging as innovative tools to improve the public health response. By utilising clinical, biological, epidemiological and environmental data, these technologies enable early case detection, patient triage, contact tracing and the prediction of outbreak trends.

Through supervised learning, deep learning and big data, expert systems improve the speed, accuracy and reliability of medical decisions. However, their effectiveness depends on data quality, staff training, cybersecurity, system interoperability and an ethical framework. The gradual integration of these systems is a strategic approach to sustainably strengthening the fight against Ebola in the DRC.

Keywords: Ebola; Democratic Republic of the Congo; artificial intelligence; expert systems; medical decision support; digital health; epidemiological surveillance; machine learning; big data.

Received Date: April 21, 2026

Accepted Date: May 12, 2026

Published Date: June 01, 2026

Available Online at: https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/752