Omar Karadaghy, MD, MSCI

Dr. Omar Karadaghy he began his medical career in 2011 by matriculating into the accelerated BA/MD program at the University of Missouri in Kansas City. During his medical training, Dr. Karadaghy took a voluntary leave of absence from 2016 to 2017 to participate in the NIH-funded TL-1 Predoctoral Training Program at Washington University in St. Louis. This program provided him with the opportunity to engage in full-time research while completing coursework for a Master of Science in Clinical Investigation.

After completing the TL-1 program, Dr. Karadaghy returned to medical school to finish his final year, earning both a Bachelor's Degree in Liberal Arts and a Doctor of Medicine (MD) degree in 2018. He subsequently matched into the Otolaryngology – Head and Neck Surgery residency program at the University of Kansas, where he trained from 2018 to 2023. During his residency, Dr. Karadaghy expanded his skill set by spearheading the development of an adjunct curriculum focused on machine learning and artificial intelligence, which included completing a Machine Learning certification through Stanford Online in 2020.

Following residency, Dr. Karadaghy pursued further specialization in Head and Neck Oncology and Microvascular Reconstruction through the Massachusetts Eye and Ear – Harvard University program, refining his expertise in complex oncologic surgeries and advanced reconstruction techniques.

Dr. Karadaghy is committed to providing high-quality patient care. His area of expertise includes all aspects of head and neck oncology, from thyroid to the upper aerodigestive tract and skull base. Furthermore, he specializes in complex reconstruction of the head and neck for deformities due to trauma, infection or malignancy.

Dr. Karadaghy offers a wide range of innovative surgical procedures, including minimally invasive robotic surgery for locations that are difficult to access. He works closely with the multidisciplinary oncology team at The University of Kansas Cancer Center and KU Medical Center.

Dr. Omar Karadaghy’s research expertise lies in clinical outcomes research, with a strong foundation established during his training. From June 2016 to May 2017, he pursued a Master of Science in Clinical Investigation, gaining the skills necessary to design and execute robust clinical studies. Dr. Karadaghy applied this knowledge extensively during his residency, where he led and contributed to numerous research projects aimed at optimizing patient management and enhancing the understanding of disease processes. A significant focus of his work was the integration of machine learning into Otolaryngology, exploring its potential applications at a time when its use in the field was still emerging.

During his fellowship, Dr. Karadaghy expanded his research focus to include robotic surgery and HPV-positive head and neck cancers. His efforts have centered on developing novel surgical techniques and refining treatment paradigms to improve oncologic outcomes. Specifically, in the context of HPV-positive disease, he investigated surgical approaches to optimize cancer control while minimizing morbidity, with a particular emphasis on surgical margins. This work reflects Dr. Karadaghy’s commitment to advancing head and neck surgery through innovative research and the adoption of cutting-edge technology.

Dr. Karadaghy actively participates in clinical research, seeking to find new technologies and new management to improve cure rates from head and neck cancer and optimizing quality of life. He is an active member of the American Academy of Otolaryngology - Head and Neck Surgery and the American Head and Neck Society, where he frequently presents his research with other surgeons across the nation.

Development and Assessment of a Machine Learning Model to Help Predict Survival Among Patients With Oral Squamous Cell Carcinoma.

Development and Validation of Machine Learning Models for Predicting Occult Nodal Metastasis in Early-Stage Oral Cavity Squamous Cell Carcinoma

Development of a New Clinical Severity Staging System for Patients With Nonmetastatic Papillary Thyroid Carcinoma.

Machine learning directed sentinel lymph node biopsy in cutaneous head and neck melanoma.

Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma

Machine Learning to Predict Treatment in Oropharyngeal Squamous Cell Carcinoma.

Prognostic significance of hemorrhage requiring embolization in the setting of previously treated head and neck squamous cell carcinoma: Systematic review and retrospective cohort.

Rates of bone reabsorption and union in mandibular reconstruction using the osteocutaneous radial forearm free flap.

Reporting of Effect Size and Confidence Intervals in JAMA Otolaryngology-Head & Neck Surgery.

Survival for HPV-positive oropharyngeal squamous cell carcinoma with surgical versus non-surgical treatment approach: A systematic review and meta-analysis.