Introduction: The traditional Halstedian apprenticeship model ("See one\, do one \, teach one") is increasingly challenged by ethical constraints\, reduced working hours\, and significant global disparities in training resources. While young otolaryngologists in major academic centers benefit from high -volume robotic and endoscopic exposure\, peers in developing regions ofte n lack access to specialized mentorship. This presentation proposes a para digm shift: leveraging Artificial Intelligence (AI) and Deep Learning (DL) not merely as diagnostic tools\, but as the great equalizer in surgical e ducation.
Methods &\ ; Analysis: We analyzed scientific studies exploring the growing role of Computer Vision (CV) and Motion Analysis algorithms in Otolaryngol ogy training. Specifically\, we examined DL models trained to objectively assess surgical videos of Endoscopic Sinus Surgery (ESS) and temporal bone dissection. These systems were evaluated on their ability to act as "Virt ual Mentors\," providing automated\, granular feedback on instrument handl ing\, tissue respect\, and operative flow compared to standard OSATS (Obje ctive Structured Assessment of Technical Skills) scores.
Results: Current Deep Learning models demonstrate the capacity to segment surgical phases and identify un safe maneuvers with accuracy comparable to expert consensus. By integratin g these AI-driven feedback loops into cloud-based simulation platforms\, r esidents can achieve proficiency benchmarks independently of their geograp hic location or local faculty availability. This technology effectively de couples high-quality surgical feedback from the physical presence of a mas ter surgeon.
Conclusion : For the Young IFOS community\, AI represents a critical opportu nity to bridge the global training gap. Transitioning from subjective appr enticeship to data-driven\, objective competence assessment fosters true g lobal diversity. By embracing these "Augmented Intelligence" tools\, we ca n ensure that the next generation of otolaryngologists achieves a standard ized level of excellence\, ensuring that patient safety and surgical skill are defined by dedication\, not geography.
DTSTAMP:20260618T223111Z LOCATION:Young IFOS 1 SUMMARY:Application of Artificial Intelligence in Otolaryngology Resident T raining - Resul Arjin Öksüz\, Alper Özdemir\, Ahmet Furkan Kürüm\, Fe rhat Deniz\, Banu Öksüz Ok URL:https://sciencenext.org/ifos2026/talk/FZ73YB/ END:VEVENT END:VCALENDAR