Vol. 11, Issue 6, Part A (2024)

Author(s): Moumita Barik, Anirban Pattanayak, Saptarshi Mukherjee, Samarpita Koner, Souvik Tewari and Shraddha Vaishnav

Abstract: This systematic review explores the potential and advancements of AI technology in the detection and diagnosis of dengue fever. Dengue, a mosquito-borne viral infection, poses significant public health challenges, particularly in tropical and subtropical regions. Traditional diagnostic methods are often time-consuming and resource-intensive, creating the need for innovative approaches. AI technologies, including machine learning and deep learning models, have demonstrated promising capabilities in enhancing early detection, improving diagnostic accuracy, and predicting outbreaks by analyzing large datasets such as clinical records, lab results, and environmental factors. This review synthesizes the current literature on AI applications in dengue detection, evaluating their effectiveness, limitations, and potential for integration into healthcare systems. Our findings suggest that while AI offers substantial improvements over conventional methods, further research is necessary to address challenges related to data availability, model generalization, and real-world implementation.

DOI: 10.22271/23487941.2024.v11.i6a.815