Associate Professor Dr. Sonal S. Shah is a board-certified oral and maxillofacial pathologist, and a private practitioner in the dental school’s Faculty Practice where she diagnoses and treats patients with a wide range of oral mucosal diseases, lesions, tumors, and cancers, along with performing soft tissue biopsies. She also signs out surgical pathology cases in the school’s Oral and Maxillofacial Pathology Biopsy Service.
Dr. Shah earned her bachelor’s in molecular biology from the University of Texas at Austin, her DDS from University of Texas at Houston School of Dentistry, and an oral pathology specialty certificate from New York Hospital Queens. She has more than 15 years of experience in academic institutions and private practice.
Prior to UNLV, Dr. Shah was a clinical assistant professor at New York University (NYU) College of Dentistry and was appointed director of the NYU Oral Medicine Clinic. After leaving NYU, she started her own clinical oral pathology private practice and maintained two busy offices in the New York City area.
Dr. Shah is a regularly invited speaker at numerous professional dental meetings including the Greater New York Dental Meeting and those for the American Dental Association (ADA). She is actively involved with the national oral pathology and oral medicine academies and holds several prominent leadership positions, notably as an ADA/Commission on Dental Accreditation oral pathology residency program site visitor, and as an appointed member of the Board of Trustees of the American Academy of Oral Medicine and the Integrated National Board Dental Examination Test Construction Committee.
Dr. Shah is an active member of the Southern Nevada Dental Society and serves as a delegate for the Nevada Dental Association. She has several publications, presentations, and posters at national meetings, and is a manuscript reviewer for several national journals. Dr. Shah is currently involved with projects involving the early detection of oral cancer through salivary biomarkers and the development of an artificial intelligence application for oral lesion diagnosis.