Abstract
Parasitic infections pose a significant public health challenge, particularly in tropical and subtropical regions, where they affect nearly a quarter of the global population. These infections can lead to various health issues, including malnutrition, anemia, and increased susceptibility to other diseases, thereby hindering development efforts. The World Health Organization highlights that a significant proportion of neglected tropical diseases are parasitic, underscoring the need for improved diagnostic methods. Early microscopy and staining techniques laid the groundwork for identifying parasites, paving the way for modern diagnostic approaches. Serodiagnostics have progressed from early 20(th)-century tests to more advanced techniques, such as enzyme-linked immunosorbent assays and immunoblot. However, challenges remain, such as cross-reactivity and the difficulty in distinguishing between past and current infections. Currently, molecular diagnostics, utilizing technologies such as polymerase chain reaction, multiplex assays, and next-generation sequencing are increasingly used to improve sensitivity and specificity in detecting parasites. In the years to come, there is a growing emphasis on integrating artificial intelligence and deep learning, particularly convolutional neural networks, which are revolutionizing parasitic diagnostics by enhancing detection accuracy and efficiency. Innovative imaging technologies are enabling faster identification of parasites and addressing traditional diagnostic limitations. However, challenges persist, including the need for diverse datasets and infrastructure support in low-resource settings. Continued research and development are essential to overcome these obstacles and ensure better global health outcomes in the face of evolving parasitic threats.