ArticleIEEE access : practical innovations, open solutions2026
Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification.
Article in IEEE access : practical innovations, open solutions, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Large Language Models (LLMs) are being widely adopted in different domains including education, healthcare, and finance. In healthcare domain, LLMs are used in disease diagnosis, abnormality classification, remedy suggestions etc. Multi-abnormality classification of radiology reports is essential in healthcare, medical decision-making, and drug discovery. LLMs are increasingly utilized for such tasks because of their remarkable Natural Language Processing (NLP) capabilities, which streamline medical report processing and reduce administrative burden. To enhance the predictive accuracy, LLMs are often fine-tuned on private, locally available datasets, such as medical reports. However, this practice raises significant privacy concerns, as LLMs are prone to memorizing training data, making them susceptible to data extraction attacks even through query-based access. Additionally, sharing fine-tuned models and weights poses adversarial risks, because they may inadvertently reveal sensitive information about the training data. Despite the growing application of LLMs to medical text classification, privacy-preserving fine-tuning for multi-abnormality classification remains underexplored. To bridge this gap, we propose a differentially private (DP) fine-tuning approach that preserves privacy while enabling multi-abnormality classification from text radiology reports through Low Rank Adaptation (LoRA). Our framework leverages DP optimization techniques to fine-tune LLMs on local patient data while mitigating data leakage risks. To our knowledge, this is the first study to incorporate DP fine-tuning of LLMs for multi-abnormality classification using text-based radiology reports. We used labels generated by a larger LLM to fine-tune a smaller LLM, accelerating inference while maintaining privacy constraints. We conducted extensive experiments on the MIMIC-CXR, and CT-RATE datasets to evaluate DP fine-tuning method across varying privacy regimes, analyzing the privacy-utility trade-off and demonstrating the efficacy of our approach. For instance, on the MIMIC-CXR dataset, our proposed DP-LoRA framework achieves weighted F1-scores of up to 0.89 under moderate privacy budgets (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.