5 citations · 5 across the 6 of their papers we have counts for
6 papers
Protecting patient privacy in clinical foundation models: Technical and legal perspectives
Sana Tonekaboni, Lena Stempfle, Sasha Ronaghi +4
Clinical foundation models trained on large-scale patient data are increasingly used for decision support, screening, and public health planning. As deployment expands, privacy ris…
Clinically Grounded Privacy Evaluation of Medical LMs
Sasha Ronaghi, Sana Tonekaboni, Lena Stempfle +6
Medical language models (LMs) can memorize and reproduce protected health information, but privacy evaluations often focus on recovery of training text rather than disclosure under…
Clinical Note Bloat Reduction for Efficient LLM Use
Jordan L. Cahoon, Chloe Stanwyck, Asad Aali +6
Background: Clinical notes contain extensive duplicated text from templates, copy-paste, and auto-populated fields ("note bloat"), diluting clinical signal, limiting longitudinal c…
Clinician input steers AI toward accurate and harmful recommendations
Ivan Lopez, Selin S. Everett, Bryan J. Bunning +10
Large language models (LLMs) are entering clinical workflows, yet evaluations rarely assess how clinician reasoning shapes model behavior during clinical interactions. Using 61 cur…
AI-generated data contamination erodes pathological variability and diagnostic reliability
Hongyu He, Shaowen Xiang, Ye Zhang +15
Generative artificial intelligence (AI) is rapidly populating medical records with synthetic content, creating a feedback loop where future models are increasingly at risk of train…
Identifying Reasons for Contraceptive Switching from Real-World Data Using Large Language Models
Brenda Y. Miao, Christopher YK Williams, Ebenezer Chinedu-Eneh +4
Prescription contraceptives play a critical role in supporting women's reproductive health. With nearly 50 million women in the United States using contraceptives, understanding th…