5 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. As deployment expands, privacy risk increas…
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…
Training-Free Adaptation of New-Generation LLMs using Legacy Clinical Models
Sasha Ronaghi, Chloe Stanwyck, Asad Aali +4
Adapting language models to the clinical domain through continued pretraining and instruction tuning requires costly retraining for each new model generation. We propose Cross-Arch…
Large Language Models for Large-Scale, Rigorous Qualitative Analysis in Applied Health Services Research
Sasha Ronaghi, Emma-Louise Aveling, Maria Levis +3
Large language models (LLMs) show promise for improving the efficiency of qualitative analysis in large, multi-site health-services research. Yet methodological guidance for LLM in…
Structured Insight from Unstructured Data: Large Language Models for SDOH-Driven Diabetes Risk Prediction
Sasha Ronaghi, Prerit Choudhary, David H Rehkopf +1
Social determinants of health (SDOH) play a critical role in Type 2 Diabetes (T2D) management but are often absent from electronic health records and risk prediction models. Most i…