AI-Powered Innovations in Pediatric TB Diagnosis and CarePediatric tuberculosis (TB) remains under-diagnosed and underreported, particularly in high-burden, resource-limited settings. In a discussion featuring Dr. Heather Zar (University of Cape Town), Dr. Celso Khosa (INS/CISPOC Mozambique), Dr. Stephen John (Janna Health Foundation, Nigeria), and Dr. Justy Anthony Chiramal (Qure.ai), experts explored how AI-powered chest X-ray analysis, digital health tools, and predictive algorithms can bridge diagnostic gaps in children under five.
Qure.ai’s AI solutions are being validated to enhance microbiological diagnosis, pediatric pneumonia detection, and comorbidity management, addressing TB alongside malnutrition and HIV. The panel highlighted the cost-effectiveness, scalability, and potential of AI to reduce inequities, improve early intervention, and optimize preventive TB treatment. AI also enhances portable X-ray accessibility, ensuring TB detection reaches underserved communities.
As pediatric TB remains a leading cause of childhood mortality, integrating AI-driven diagnostics into global health programs could revolutionise early detection, treatment pathways, and healthcare equity worldwide.
Qure.ai’s AI solutions are being validated to enhance microbiological diagnosis, pediatric pneumonia detection, and comorbidity management, addressing TB alongside malnutrition and HIV. The panel highlighted the cost-effectiveness, scalability, and potential of AI to reduce inequities, improve early intervention, and optimize preventive TB treatment. AI also enhances portable X-ray accessibility, ensuring TB detection reaches underserved communities.
As pediatric TB remains a leading cause of childhood mortality, integrating AI-driven diagnostics into global health programs could revolutionise early detection, treatment pathways, and healthcare equity worldwide.
