Clifton David Fuller

Clifton David Fuller is a Professor in the Department of Radiation Oncology at The University of Texas MD Anderson Cancer Center, where he directs the Human Imaging Research Office and co-leads the Big Data and Informatics Program. He is internationally recognized as a pioneer in applying artificial intelligence and machine learning to radiation therapy workflows, with a particular focus on automating the labor-intensive process of organ-at-risk and tumor volume delineation in head and neck cancers. His research program integrates deep learning, natural language processing, and radiomics to transform how radiation oncologists plan and adapt treatments. Fuller leads several landmark multi-institutional consortia, including the MD Anderson-led Radiation Planning Assistant (RPA) project, which has developed and clinically validated fully automated radiation treatment planning workflows for low- and middle-income countries. He has been a principal investigator on numerous NIH, NCI, and CPRIT-funded grants, and has led prospective clinical trials examining adaptive radiotherapy, MRI-guided treatment, and patient-reported outcomes in head and neck malignancies. His team's open-science approach has produced publicly available datasets and models that have accelerated the global adoption of AI-assisted contouring. Fuller is the recipient of multiple early-career and mid-career awards from the American Society for Radiation Oncology (ASTRO) and the American Head and Neck Society. He has published extensively in journals including Nature Medicine, the Journal of Clinical Oncology, Radiology, and the International Journal of Radiation Oncology Biology Physics. His work exemplifies the convergence of clinical expertise, large-scale data science, and translational commitment that defines the modern era of precision radiation oncology.