Dari Simulasi ke Praktik: Pengaruh Robotika Berbasis AI terhadap Pendidikan Medis dalam Bedah Kepala dan Leher. - Rizki Saputra; Ricvan Dana Nindrea; Maudi Octarini Ezeddin; Yosa Tamiya Marisa (Oto Rhino Laryngologica Indonesiana, 2026)
Background: Robotic surgery has become increasingly relevant in head and neck procedures, offering enhanced precision, improved access to anatomically complex regions, and favorable functional and cosmetic outcomes. Technological advances, including the integration of artificial intelligence (AI) and the emergence of novel platforms, have expanded the scope of minimally invasive surgery in Otolaryngology. However, disparities in access, training, and evidence quality, remain barriers to widespread adoption. Purpose: To evaluate the current impact of robotic surgery in head and neck surgery by synthesizing recent evidence on its clinical applications, technological development, training infrastructure, and future directions. Method: A narrative literature review was conducted using PubMed, Scopus, and Web of Science databases to identify peer-reviewed articles published between January 2010 and February 2025. Ten studies were selected based on relevance to robotic surgery in head and neck procedures. Data were extracted regarding study design, participants, intervention, outcomes, and thematic contributions. Result: The reviewed studies consistently demonstrated that robotic surgery was feasible and safe for transoral procedures, neck dissections, and pediatric cases. New platforms like Versius and KD-SR-01 showed potential for improved accessibility and usability. Key barriers included high cost, limited training availability, and lack of randomized controlled trials. AI applications remain largely theoretical with minimal clinical integration. Conclusion: Robotic surgery in head and neck practice is advancing with strong clinical promise. Future efforts must focus on cost-effectiveness, standardized training, and translational research to optimize access and impact, especially in under-resourced settings