# Challenges of Artificial Intelligence in Medical Diagnosis in Congolese Hospitals: A Literature Review

## TL;DR

This paper reviews the potential and challenges of using AI for medical diagnosis in Congolese hospitals, highlighting the need for infrastructure and training.

## Contribution

The paper provides a focused literature review on AI in medical diagnosis in the DRC, emphasizing local challenges and opportunities.

## Key findings

- AI improves diagnostic accuracy by 12%-15% in radiology and reduces exam interpretation time by 20%.
- AI can accelerate epidemic detection by 30%-50% compared to conventional methods.
- Implementation in the DRC is hindered by poor infrastructure, lack of training, and weak regulations.

## Abstract

Artificial intelligence (AI) is rapidly transforming medical diagnosis worldwide, but its adoption remains limited in Africa, particularly in the Democratic Republic of Congo (DRC). This narrative review aims to analyze the contributions, challenges, and prospects for integrating AI into medical diagnosis in the DRC.

A comprehensive literature review was conducted in February 2025 in PubMed, Web of Science, Scopus, and Google Scholar databases, as well as reports from international organizations. Studies on the use of AI in medical diagnosis in resource‐limited countries, particularly in Africa, were included without language restrictions. The selection followed a two‐step process (title/abstract then full text); 103 articles were retained for qualitative synthesis.

Studies show that AI enables a 12%–15% improvement in diagnostic accuracy in radiology and a 20% reduction in exam interpretation time. It also helps accelerate epidemic detection (30%–50% faster than conventional methods) and overcome the shortage of specialists in rural areas. However, its implementation in the DRC is hampered by the lack of digital infrastructure, insufficient training, and the absence of an appropriate regulatory framework. Maintenance and financing issues still limit the effective use of available systems.

AI represents a major opportunity to strengthen medical diagnosis in the DRC, improving the speed and quality of care. However, effective integration requires targeted investments in infrastructure, training, and regulation. The development of national pilot projects and a solid ethical framework are essential steps for gradual and sustainable adoption.

Artificial intelligence can improve diagnostic accuracy, efficiency, and epidemic surveillance in resource‐limited settings. However, its adoption in Congolese hospitals remains constrained by infrastructure gaps, limited training, financial barriers, and weak regulatory frameworks.