[2506.04450] Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification

Computer Science > Cryptography and Security arXiv:2506.04450 (cs) [Submitted on 4 Jun 2025 (v1), last revised 30 Mar 2026 (this version, v5)] Title:Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification Authors:Payel Bhattacharjee, Fengwei Tian, Geoffrey D. Rubin, Joseph Y. Lo, Nirav Merchant, Heidi Hanson, John Gounley, Ravi Tandon View a PDF of the paper titled Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification, by Payel Bhattacharjee and 7 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) are increasingly adopted across domains such as education, healthcare, and finance. In healthcare, LLMs support tasks including disease diagnosis, abnormality classification, and clinical decision-making. Among these, multi-abnormality classification of radiology reports is critical for clinical workflow automation and biomedical research. Leveraging strong natural language processing capabilities, LLMs enable efficient processing of unstructured medical text and reduce the administrative burden of manual report analysis. To improve performance, LLMs are often fine-tuned on private, institution-specific datasets such as radiology reports. However, this raises significant privacy concerns: LLMs may memorize training data and become vulnerable to data extraction attacks, while sharing fine-tuned models risks exposing sensitive patient information. Despite growing interest in LLMs for medical tex...