Artificial intelligence for the diagnosis and prognosis of leprosy, leishmaniasis, and schistosomiasis: A systematic review

Background Neglected Tropical Diseases affect over one billion individuals globally, challenging healthcare systems in endemic regions. While promising, Artificial Intelligence (AI) requires rigorous validation for clinical translation. Methodology This systematic review of prediction-model studies evaluated AI applications for leprosy, schistosomiasis, and leishmaniasis (2015–2025). Methodological quality and reporting transparency of the included studies were appraised using the PROBAST+AI and TRIPOD+AI tools, respectively. Findings Among 69 studies identified, 89.9% were published after 2020. The literature predominantly focused on diagnostic support (81.2%), leaving the prognosis of adverse outcomes underexplored (18.8%). Methodological fragility was widely observed: 89.9% of models exhibited a high risk of bias, primarily due to lack of external validation (82.6%) and failure to report or appropriately handle missing data (66.7%). Transparency remains a barrier, with source code unavailable in 76.8% of cases, hindering reproducibility. Conclusion Current evidence indicates that quantitative output outpaces methodological maturity. Bridging the gap between algorithmic potential and public health impact requires prioritizing open science and robust validation.