Ti Bai, PhD

Online adaptive prostate MR-guided radiotherapy (MRgRT) is time-sensitive, and contouring with structure preparation can require upwards of 15 minutes per fraction. While vendor-TPS provided contours can be useful, performance and consistency vary by site, pr...

Cancer treatment planning requires clinicians to rapidly synthesize complex clinical information from detailed patient notes, a process that is time-consuming and cognitively demanding, particularly in multidisciplinary workflows involving non-physician clini...

This work develops an agentic AI framework that bridges the gap between state-of-the-art tumor segmentation models and clinical deployment, where model discovery, data preprocessing, and output QA remain time-intensive and require computational expertise. Thi...

While deep learning autosegmentation models are widely integrated into clinical workflows in radiation oncology, a critical gap has emerged: the "static deployment" trap. Once deployed, model performance can deteriorate due to real-world data evolution, makin...

AI-based autosegmentation is widely used in radiation oncology to improve efficiency and consistency; however, these models may silently fail when applied to cases that deviate from their training distribution, placing responsibility on clinicians to detect u...