Artificial intelligence for tuberculosis management in Africa: opportunities, challenges, and implementation

Area of Science:

• Public Health
• Infectious Disease Management
• Artificial Intelligence in Healthcare

Background:

• Tuberculosis (TB) remains a major global health challenge, particularly in African nations.
• Limited diagnostic accuracy and insufficient healthcare systems exacerbate TB transmission and treatment failures.
• Conventional diagnostic methods often prove inadequate for effective TB control in resource-limited settings.

Purpose of the Study:

• To explore the potential of artificial intelligence (AI) in enhancing TB diagnosis, management, and control in African countries.
• To review the limitations of current TB diagnostic tools and the emerging role of AI in healthcare.
• To assess how AI can improve clinical decision-making and surveillance for TB.

Main Methods:

• A narrative review of existing literature on TB management challenges in Africa.
• Focus on limitations of conventional diagnostic tools (e.g., tuberculin test, radiography, microscopy).
• Analysis of emerging AI technologies, including machine learning and computer-aided systems, for healthcare applications.

Main Results:

• Conventional TB diagnostics exhibit limited accuracy and efficiency, contributing to poor outcomes.
• AI innovations show promise for improved disease detection, prediction, and resource efficiency.
• AI can aid clinicians in diagnosis, forecast treatment outcomes, and strengthen public health strategies against TB.

Conclusions:

• AI offers significant potential to enhance TB control efforts in African countries.
• Improved diagnostic precision, clinical decision-making, and surveillance capacities are key benefits of AI integration.
• Integrating AI into national TB programs can lead to more effective disease management and reduced morbidity/mortality.