Future directions for otorhinolaryngology residency in the age of artificial intelligence: a review article

Abstract Background Artificial intelligence (AI) has achieved significant breakthroughs in various areas of medicine; however, its integration into otorhinolaryngology (ORL) residency training remains limited. AI can offer many opportunities to enhance ORL residency programs through better study methods, research facilitation, and clinical skills development. Methodology This review examines current literature on the possible future applications of AI in otolaryngology residency training, focusing on educational support, clinical applications, and research productivity. Studies on large language models (LLMs), deep learning (DL) platforms, and survey-based evaluations of specialists and residents were analyzed to highlight emerging trends, opportunities, and limitations. Results AI technologies such as large language models (LLMs) and deep learning (DL) can offer case-based simulations, exam preparation support, and automated feedback on surgical skills. They can also ease research by making literature reviews more efficient, strengthening data analysis, and helping refine study designs. Surveys show strong support among otolaryngology specialists and trainees for AI integration, though concerns remain about reliability, trust, and ethical use. Key limitations include the “black box” nature of algorithms, limited datasets, and unfamiliarity with AI tools. Conclusion Despite these challenges, AI holds great promise in supporting traditional teaching, enhancing diagnostic accuracy, and enriching clinical training in ORL residency programs. To achieve this, it must be implemented responsibly, with clear attention to transparency and ethical considerations.