Konstantina M. Stankovic
Konstantina Stankovic, MD, PhD, FACS, is the Bertarelli Foundation Professor and Chair of the Department of Otolaryngology–Head and Neck Surgery at Stanford University School of Medicine, and professor, by courtesy, of Neurosurgery. She is a Harvard-trained ear and skull-base surgeon and a Massachusetts Institute of Technology-trained auditory neuroscientist. She blends her surgical expertise with training in physics, molecular biology, auditory neuroscience, and systems electrophysiology to devise novel solutions tailored to the unmet needs of those with hearing loss. She is an elected member of the US National Academy of Medicine and President-Elect of the Association for Research in Otolaryngology.
Sessions
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.