A comparative look at how emerging technologies evolve to managing otitis media

Abstract

Otitis media (OM) is an epidemic of middle ear infection in tens of millions of patients across the globe, most vulnerable of whom are children, with hearing loss and other negative consequences unless treated. Conventional diagnosis and treatment are marred by failure to diagnose, service shortage, and delayed diagnosis. This present paper is directed towards a comparative outlook of the newly emerging technologies, such as artificial intelligence (AI), machine learning, telemedicine, and wearable biosensors, that are revolutionizing the management of OM. We emphasize the way such devices enhance diagnostic accuracy, facilitate remote and real-time monitoring, and provide tailored treatment schemes. Our approach is more sophisticated compared to the currently available state-of-the-art methods reported in the literature based on real-time telemedicine systems, multimodal data fusion, and interpretable AI. Privacy issues of information, model generalizability issues, and technological adoption barriers are also discussed. The results also substantiate that adoption of these advanced devices can effectively reduce OM's burden globally and improve patient outcomes.