Noel Ayoub
Noel Ayoub, MD, MBA is a Clinical Assistant Professor at Stanford University in the Department of Otolaryngology–Head and Neck Surgery. He received his medical degree and MBA from Stanford, followed by residency training in Otolaryngology–Head and Neck Surgery at Stanford Health Care and fellowship training in Rhinology and Skull Base Surgery at Massachusetts General Hospital and Mass Eye and Ear/Harvard Medical School. His research focuses on healthcare innovation and health systems leadership, with a particular emphasis on applying AI and machine learning to improve patient care, optimize hospital operations, and reduce costs.
Sessions
Artificial Intelligence Applications in the Diagnosis and Management
Olfactory Neuroblastoma: Currents advances in diagnosis and treatment
Artificial intelligence (AI) is rapidly transforming healthcare, and otolaryngology is no exception. From diagnostic imaging and voice recognition to surgical decision-making and population health analysis, AI offers the potential to improve care delivery, enhance precision, and optimize efficiency across the spectrum of otolaryngology care. This symposium will explore the current and emerging applications of AI in otolaryngology, with a focus on real-world clinical use cases, implementation barriers, ethical risks, and translational research.
The session will feature four expert talks followed by a panel discussion. Each speaker will present a distinct perspective on AI’s integration into otolaryngology —including clinical applications, hospital operations, data governance, and regulatory frameworks. We will highlight examples such as AI for automated diagnostic triage, operative note and billing optimization, AI-powered telehealth tools, and the development of multimodal datasets to train otolaryngology-specific algorithms. A key theme will be separating evidence-based progress from exaggerated claims, while identifying pragmatic pathways to safely and ethically incorporate AI into otolaryngology practice.
Attendees will leave with an updated understanding of where AI is most effective in otolaryngology, the infrastructural and ethical challenges to deployment, and how to participate in or lead future AI-driven research and innovation in their own institutions.
Learning Objectives:
Identify high-impact clinical applications of AI in otolaryngology.
Understand the technical and operational barriers to safe AI implementation in ENT care.
Evaluate ethical considerations, such as algorithmic bias, explainability, and patient trust.
Explore future directions in AI-driven research, including digital twins, automated clinical documentation, and real-world evidence generation.