Enhancing Models for Breast Cancer Risk Prediction Through Clinician-AI Collaboration

Breast cancer is the second leading cause of death among women in the U.S., with Black women experiencing higher mortality than any other racial group. While advances in artificial intelligence (AI) and deep learning (DL) have shown promise in improving individual risk assessment and early detection, concerns about “AI bias” raise questions about patient outcomes, healthcare costs, and equitable care.

This research symposium will convene invited speakers from artificial intelligence, medical imaging, clinical medicine, and public health to present their latest findings on overcoming these challenges. Participants will learn how collaborations between clinicians and AI researchers are helping to identify and mitigate bias, improve predictive accuracy, and ensure tools work effectively across diverse patient populations. The symposium will also explore the use of large language models and other innovative approaches to generate hypotheses about sources of underperformance and hidden bias in AI models.

Attendees will gain insights into current strategies, ongoing research, and practical applications for making AI-driven breast cancer detection more reliable and equitable. This symposium is relevant for researchers, healthcare professionals, and anyone interested in the intersection of AI, oncology, and health equity.