John Neylon

John Neylon is an HS Assistant Clinical Professor in the Department of Radiation Oncology at the University of California, Los Angeles (UCLA). His research focuses on advancing radiation therapy techniques through medical physics innovations and AI-driven solutions. Key areas include image-guided radiotherapy, automated image review systems, and optimization of radiation delivery protocols. He collaborates extensively with peers in radiation oncology, medical physics, and computational sciences to enhance treatment precision and patient outcomes.

His work integrates cutting-edge technologies such as deep learning models for medical imaging segmentation and AI-assisted decision-making in radiation therapy. Recent studies involve improving patient alignment accuracy through automated systems and analyzing motion management in prostate and gynecological cancers. He also explores the dosimetric impacts of anatomical changes during treatment, contributing to safer and more effective radiation protocols.

Neylon’s publications highlight advancements in MRI-guided stereotactic body radiotherapy, cone beam CT applications, and cloud-based dose calculation frameworks. His research bridges clinical practice and computational innovation, aiming to reduce treatment side effects and improve treatment planning efficiency.