A Review on an AI-Powered System for Detection of Malaria

Malaria is still a major health issue around the world, especially in areas where it is common. This highlights the need for fast and accurate diagnostic methods to support traditional microscopy and rapid tests. Recent developments in Artificial Intelligence (AI) and Deep Learning (DL) show great promise in automating malaria detection using blood smears, clinical features, spectroscopy, and blood data. This review brings together findings from various studies that use convolutional neural networks, transfer learning, ensemble models, data augmentation, and hybrid AI methods. Reported accuracies are consistently over 95%, with some models getting near-perfect results on standard datasets. However, there are still important gaps, including limited external validation, dependence on publicly available celllevel datasets, a lack of species-level differentiation in many methods, and insufficient exploration of real-world deployment issues. New solutions like smartphone-based diagnostics, near-infrared spectroscopy, and improved hybrid models provide fresh ideas. This paper discusses current progress, points out key limitations, and suggests future directions for strong, field-ready AI-driven malaria diagnostics.