Abstract: Fungal skin infections are an emerging public health issue in India, with millions of people being affected every year by diseases like Tinea capitis, vaginal candidiasis, and aspergillosis. This review delves into the two sides of this challenge—evaluating the projected disease burden and discussing the emergence of Machine Learning (ML) and Deep Learning (DL) tools in fungal skin disease research during the period from 2018 to 2023. Epidemiological findings demonstrate a widespread incidence of superficial and systemic fungal infections, emphasizing the need for increased awareness, early detection, and efficient treatment approaches. At the same time, the review points to a significant rise in ML/DL-based research, indicating an intensifying interest in using artificial intelligence for dermatologic diagnosis. The convergence of public health and technology implies potential prospects for enhancing outcomes through AI-assisted tools, as long as they are supplemented by strong clinical validation and health policy infrastructure. The research calls for a multi-disciplinary solution to address India's increasing burden of fungal disease.
Keywords: Fungal skin diseases, India, Tinea capitis, vaginal candidiasis, machine learning, deep learning, dermatology, artificial intelligence, epidemiology, disease burden.
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DOI:
10.17148/IJARCCE.2025.14684
