Titles and Affiliations
Assistant Professor of Medicine
Research Area
Advancing personalized breast cancer care through artificial intelligence risk and treatment prediction tools.
Impact
Breast cancer outcomes are not equal. Patients from underrepresented and understudied groups, as well as those in low-resource settings, often face higher risks of later diagnosis, recurrence, and poor survival. Dr. Howard aims to make breast cancer treatment more precise and equitable by using artificial intelligence (AI) to predict recurrence risk and response to therapy. By incorporating diverse patient data, digital pathology images, and advanced deep learning, the tools will be designed to work reliably across racial and ethnic groups and in low-resource settings.
What’s Next
Dr. Howard and his team will refine their clinical risk model with updated data, build multimodal AI systems using information from over 8,000 patients, and ensure that the tools predict risk but also explain its decision-making process. The team will also develop models to forecast response to chemotherapy and immunotherapy before surgery, ultimately delivering validated tools to help physicians across diverse healthcare settings make more precise, affordable treatment decisions worldwide.
Biography
Frederick Howard, MD is an Assistant Professor at the University of Chicago and a physician-scientist focusing on computational methods to improve breast cancer treatment. He received his medical degree from University of Michigan, completed his residency training at Yale University, and was named an Elwood V. Jensen Scholar at University of Chicago to support his post-fellowship research training. Dr. Howard’s work focuses on developing artificial intelligence (AI)-based tools to improve the precision and equity of breast cancer care. His research centers on integrating clinical, pathologic, genomic and imaging data to build computational models to guide treatment decisions. His work includes the development of digital assays for recurrence risk and chemotherapy benefit, aiming to create affordable, scalable alternatives to traditional genomic tests. A major aim of his efforts is ensuring these technologies are accessible in diverse patient populations and resource-limited settings, addressing disparities in cancer outcomes. He has conducted foundational work on bias derived from digital image batch effects on deep learning histology models.
Dr. Howard has published over 40 manuscripts in high impact medical journals and leads biomarker development studies at the University of Chicago. He is a recipient of multiple research awards and received a “40 Under 40 in Cancer” award in 2024. Through his work, Dr. Howard is committed to transforming breast cancer treatment through technology driven innovation, with the ultimate goal of improving outcomes for patients around the world.
