<![CDATA[Background: Otitis media (OM) is a common middle ear infection that can lead to hearing and developmental impairments. Although artificial intelligence (AI) achieves 76-98.26% accuracy in image-based diagnosis, its utilization as a chatbot for triage and education remains very limited.Objectives: This study aims to map AI research trends in OM diagnosis, evaluate evidence of AI effectiveness, and identify gaps for clinical chatbot design.Methods: The study employed a mixed-methods approach with bibliometric analysis and systematic literature review following PRISMA 2020 guidelines on PubMed articles from 2021-2025.Results: The systematic review of 12 studies revealed research dominance by the United States and China, focusing on digital otoscopy and image classification. However, only one study mentioned the potential of large language models (LLMs), and no clinically validated OM chatbot prototypes were found. Conclusion: There exists a critical gap between image-based diagnostic capabilities and accessible text/speech-based solutions for the public. This study proposes developing a clinical OM chatbot that integrates current medical evidence and validated LLM architecture. This development aims to enhance healthcare access, improve initial triage accuracy, and provide patient education in regions with limited ENT specialists. The systematic review and gap analysis-based approach serves as an important methodological foundation for ensuring clinical relevance and sustainable implementation of chatbots in healthcare systems]]>
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