Allie Reid, MD

Allison Reid

Specialties

  • Gynecologic Oncology

Treatment Philosophy

I strive to provide compassionate, evidence-based care that reflects the unique needs, values, and preferences of each individual patient. I believe that innovative, equitable oncologic care is best delivered through a collaborative, multidisciplinary team that brings together diverse expertise. I am also passionate about education and believe that knowledge empowers patients to make informed decisions. 

Educational Background

  • Fellowship, Gynecologic Oncology, University Hospitals Cleveland Medical Center/Case Western Reserve University, Cleveland, OH
  • Residency, Obstetrics and Gynecology, University Hospitals Cleveland Medical Center/Case Western Reserve University, Cleveland, OH
  • MD, Case Western Reserve University School of Medicine, Cleveland, OH

Misc

 

About

Allison Brady Reid, MD, is an Assistant Professor in the Department of Surgery in the Division of Gynecologic Oncology at Fox Chase Cancer Center. She brings strong expertise in gynecologic oncology, surgical outcomes research, and medical education, with a focus on improving care for patients with gynecologic malignancies and advancing data-driven approaches to cancer treatment and screening. At Fox Chase, Dr. Reid will see patients at the Main Campus and Temple University Hospital—Main Campus.

Dr. Reid joins Fox Chase following fellowship training in gynecologic oncology at University Hospitals Cleveland Medical Center and Case Western Reserve University. She previously completed her residency in obstetrics and gynecology at the same institution, where she served as Administrative Chief Resident and Junior Education Chief Resident, helping lead residency education, curriculum development, and trainee support.

She earned her medical degree from Case Western Reserve University School of Medicine and holds a Bachelor of Science in Biochemistry, magna cum laude, from Boston College. Dr. Reid’s research focuses on gynecologic malignancies, including endometrial and vulvar cancer outcomes, pathologic predictors of disease progression, and the application of machine learning and artificial intelligence in ovarian cancer risk stratification and screening.