<p>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.</p>

<p>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</p>

<p>I am an otolaryngologist actively engaged in both clinical practice and academic research in the field of Ear, Nose and Throat (ENT) diseases. My work spans multiple areas of otolaryngology, with a particular focus on laryngology and voice disorders.</p> <p>My academic interests center on the evaluation and management of laryngeal pathologies and voice disorders across different age groups. I am especially interested in integrating evidence-based approaches into daily clinical practice. In addition, I have contributed to research on laryngeal imaging, voice assessment methodologies, and the clinical application of emerging technologies, including artificial intelligence, in ENT.</p> <p>I regularly participate in national and international scientific meetings, contributing to academic exchange and fostering collaborative advancements within the otolaryngology community.</p>

<p>Dr. Habib Zalzal is an otolaryngologist based in Washington, DC, with subspecialization in pediatric otolaryngology. He completed his fellowship in Pediatric Otolaryngology at Children's National Hospital/George Washington University in 2021 and his residency in Otolaryngology - Head and Neck Surgery at West Virginia University in 2020, having graduated from Georgetown University School of Medicine in 2015. His recent publications include studies on AI applications in ENT, pediatric surgical outcomes, and a quality improvement initiative in pediatric tracheostomy, among others, with works published in reputable journals such as The Laryngoscope & JAMA Otolaryngology--Head & Neck Surgery.</p>

<p>Medical Degree: Yuzuncu Yıl University Faculty of Medicine, Van, Turkiye, 2002-2008</p> <p>Residency: Inonu University Faculty of Medicine, Otorhinolaryngology and Head and Neck Surgery Department, Malatya, Turkiye, 2010-2014</p> <p>Experience: Dicle University Faculty of Medicine, Otorhinolaryngology and Head and Neck Surgery Department, Diyarbakır, Turkiye, 2022- present</p> <p>Fellow program</p> <p>1) Transoral Laser Surgery and Head and Neck Surgery (as a fellow), in Dışkapı Research and Training Hospital (three months), Ankara, Turkey, 2021</p> <p>2) University Otolaryngology Clinic at San Giovanni Bosco Hospital during the period January 1st → March 31st, (three months), Turin, Italy, 2025</p> <p>Publications</p> <p>1) Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy? Can S, Türk Ö, Ayral M, Kozan G, Arı H, Akdag M, Baylan MY . European Archives of Oto-Rhino-Laryngology (2024) 281:359–367</p> <p>2) Is the Ensemble Machine Learning Model a Reliable Method for Detecting Neoplastic Infiltration of Thyroid Cartilage in Laryngeal Cancers? Can S, Türk Ö, Ayral M, Kozan G, Onur M, Yagız E, Akdag M. Medicina (2025). 61(11), 1945.</p> <p>3) Role of machine learning segmentation method based on CT images in preoperative staging of oral cavity cancer. Can S, Succo G, Coskun C, Korkmaz MH, Akdag M. European Archives of Oto-Rhino-Laryngology (2025). https://doi.org/10.1007/s00405-025-09824-9</p>