3 papers
cs.CV2026
A Generalist Model for Diverse Text-Guided Medical Image Synthesis
Joseph Cho, Mrudang Mathur, Cyril Zakka +15
Deep learning algorithms require extensive data to achieve robust performance. However, data availability is often restricted in the medical domain due to patient privacy concerns.…
cs.CY2025
Aligning Large Language Models with Healthcare Stakeholders: A Pathway to Trustworthy AI Integration
Kexin Ding, Mu Zhou, Akshay Chaudhari +2
The wide exploration of large language models (LLMs) raises the awareness of alignment between healthcare stakeholder preferences and model outputs. This alignment becomes a crucia…
cs.CV2025
Eyes Tell the Truth: GazeVal Highlights Shortcomings of Generative AI in Medical Imaging
David Wong, Bin Wang, Gorkem Durak +23
The demand for high-quality synthetic data for model training and augmentation has never been greater in medical imaging. However, current evaluations predominantly rely on computa…