Clinical Report: Evaluating Privacy Risks in Federated Learning for Radiology Reports
Background
Radiology reports are critical for medical diagnostics, providing essential clinical insights that complement imaging data. The rise of AI, particularly large language models (LLMs), has transformed the analysis of unstructured data in radiology. However, the sharing of sensitive healthcare data is restricted by stringent privacy regulations, making federated learning a promising approach for collaborative model development while maintaining data confidentiality.
Data Highlights
Recent evaluations indicate that the use of federated learning in radiology can significantly reduce the risk of data exposure while enabling collaborative model training. Studies show that the choice of tokenizer can influence the effectiveness of privacy measures, with domain-adapted tokenizers demonstrating improved performance in safeguarding sensitive information.
Key Findings
Federated learning (FL) allows for decentralized training of models without raw data exchange, addressing privacy concerns.
Tokenizer choice significantly affects reconstruction risk under gradient-inversion attacks, with domain-adapted tokenizers performing better than general-purpose ones.
Secure aggregation and differential privacy measures may be necessary to comply with HIPAA and GDPR in federated learning applications.
Recent studies indicate that privacy attacks relevant to FL for clinical text are increasing.
Model-level vulnerabilities in FL extend beyond metadata protection.
Clinical Implications
Healthcare professionals should be aware of the privacy risks associated with federated learning in radiology. Implementing advanced security measures, such as secure aggregation and differential privacy, is essential to protect patient data while utilizing AI technologies.
Conclusion
The evaluation of privacy risks in federated learning for radiology reports highlights the importance of safeguarding patient confidentiality in AI applications.
Related Resources & Content
Castillo C, Steffens T, Sim L, Caffery L, J Med Radiat Sci, 2021 -- The effect of clinical information on radiology reporting: a systematic review
Thirunavukarasu AJ, et al., Nat Med, 2023 -- Large language models in medicine
Shool S, et al., BMC Med Inform Decis Mak, 2025 -- A systematic review of large language model (LLM) evaluations in clinical medicine
McMahan B, et al., AISTATS, 2017 -- Communication-efficient learning of deep networks from decentralized data
RSNA, 2025 -- Best Practices for the Safe Use of Large Language Models and Other Generative AI in Radiology
European Radiology — Simplifying radiology reports with large language models: privacy-compliant open- versus closed-weight models
European Radiology — Evaluating Large Language Models for Identifying Errors in Radiology Reports: A Comparison of Proprietary and Privacy-Conscious Open-Source Approaches
asco ai in oncology — Is Federated Learning the Answer to Patient Privacy in AI?
JMIR Medical Informatics — Improving Radiology Report Error Detection Using a Multipass Large Language Model: Framework Development and Validation
Is Federated Learning the Answer to Patient Privacy in AI?
Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks
Best Practices for the Safe Use of Large Language Models and Other Generative AI in Radiology | Radiology
