Daniel Lee
Dr. Daniel Lee is a rhinologist and skull base surgeon in the Division of Rhinology in the Department of Otolaryngology – Head & Neck Surgery at the University of Toronto. He completed his residency training at the University of Toronto before pursuing an advanced fellowship in rhinology and endoscopic skull base surgery at the University of Pennsylvania. Dr. Lee’s clinical and academic focus is in advanced rhinology and endoscopic skull base surgery. He has specific focus in the use of artificial intelligence in the field of rhinology and skull base surgery in addition to clinical outcomes and epidemiological studies
Sessions
Description: This roundtable will delve into the practical application of artificial intelligence (AI) within Otolaryngology - Head & Neck Surgery (OHNS). We will explore the transformative potential of AI across various subspecialties (otology, rhinology, laryngology, head & neck oncology), highlighting current examples of successful implementation and real-world use. The session will critically examine the key challenges hindering real-world adoption, including issues of accuracy, deliverable integration, data governance, interoperability, regulatory hurdles, and reimbursement models. Finally, we will discuss promising future directions and the necessary steps to bridge the gap between AI's theoretical promise and its routine clinical integration
Outcome Objectives:
To gain an understanding of applications of AI across different subspecialties within OHNS with real-world examples.
To identify and critically analyze the major challenges/barriers to the successful deployment and integration of AI in clinical practice.
To discuss strategies to overcome these challenges and barriers.
To explore future directions and emerging trends in AI for OHNS
Structure of the Session (60 minutes):
Introduction & Promise of AI (10 mins): Brief overview of AI's potential in OHNS and the objectives of the roundtable
Challenges and Barriers (20 mins): Focused discussion on the key hurdles to real-world AI deployment, including accuracy, deliverable integration, data governance, interoperability, bedside to clinical translation, regulations, and reimbursement.
Successful Implementation and Use (20 mins): Presentation of specific examples of successful AI implementation in real-life settings, highlighting key learnings and outcomes.
Concluding Remarks and Future Work (10 mins): Conclusion and brief overview of promising future directions in AI for OHNS as well as Q & A session