Artificial Intelligence in Otolaryngology: Deep Learning for Automated Ear Infection Detection
Abstract
The impact of machine learning (ML) and artificial intelligence (AI) technologies on ear infection detection and treatment is assessed in this study. In addition to analysing the legislative, technological, and ethical challenges associated with their adoption, this investigation looks at how AI/ML improves automated detection while offering clinical support and customised therapeutic solutions for the management of ear infections.
Methods: Three academic databases—PubMed, IEEE Xplore, and Scopus—were used to conduct a comprehensive review of scientific publications between January 2018 and August 2024. Using otoscopic pictures, medical histories, and real-time patient data, the study focused on research articles that explored the use of AI and ML in diagnosing ear infections. While assessing the technological difficulties and service regulations for clinical AI applications, the study examined research on AI prediction methods of ear infection development as well as strategies for healthcare decision-making support.
Results: The diagnosis and treatment of ear infections have been significantly enhanced by the integration of AI and ML technologies. Compared to manual examination methods, CNNs' deep learning models provide automated otoscopic image analysis, which is faster and more accurate. The artificial intelligence models work with high precision to diagnose otitis media and otitis externa infections and they cut down diagnostic timelines by 50% which leads to enhanced patient results. mechanical thrombectomy, improving therapeutic approaches.
Conclusion: Resolving issues with data quality, improving the clarity of AI algorithm operations, integrating AI into medical practice workflows, and removing bias are all critical concerns for AI deployments in healthcare. To create safe and equitable use practices, a careful analysis of patient confidentiality requirements and AI application ethics in medical practice is required.
