ArticleScientific reports2025
Evaluating GPT models for clinical note de-identification.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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Who cites it
10 citing papers in PubMed.
- Generative AI and Clinicians Show Comparable Prognostic Reasoning From Clinical Narratives in Biologic-Treated CRSwNP.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Mapping Machine Learning-Driven Cybersecurity Solutions in Health Care: Scoping Literature Review.Journal of medical Internet research · 2026Article
- Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance.Genomics & informatics · 2026Article
- Bridging the performance gap: systematic optimization of local LLMs for Japanese medical PHI extraction.Scientific reports · 2026Article
- Benchmarking transformer-based models for medical record de-identification in a single center multi-specialty evaluation.iScience · 2025Article
- Clinical Assessment of Fine-Tuned Open-Source LLMs in Cardiology: From Progress Notes to Discharge Summary.Journal of healthcare informatics research · 2025Article
- Considerations for Patient Privacy of Large Language Models in Health Care: Scoping Review.Journal of medical Internet research · 2025Article
- One Step Closer to Conversational Medical Records: ChatGPT Parses Psoriasis Treatments from EMRs.Journal of clinical medicine · 2025Article
- Privacy-by-Design Framework for Large Language Model Chatbots in Urology.International neurourology journal · 2025Article
- Guardians of the data: NER and LLMs for effective medical record anonymization in Brazilian Portuguese.Frontiers in public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid digitalization of healthcare has created a pressing need for solutions that manage clinical data securely while ensuring patient privacy. This study evaluates the capabilities of GPT-3.5 and GPT-4 models in de-identifying clinical notes and generating synthetic data, using API access and zero-shot prompt engineering to optimize computational efficiency. Results show that GPT-4 significantly outperformed GPT-3.5, achieving a precision of 0.9925, a recall of 0.8318, an F1 score of 0.8973, and an accuracy of 0.9911. These results demonstrate GPT-4's potential as a powerful tool for safeguarding patient privacy while increasing the availability of clinical data for research. This work sets a benchmark for balancing data utility and privacy in healthcare data management.
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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.