Evidence map›Paper›PMID 39480533›Full record

ArticleEuropean radiology2025

Automated anonymization of radiology reports: comparison of publicly available natural language processing and large language models.

Marcel C Langenbach, Borek Foldyna, Ibrahim Hadzic, Isabel L Langenbach, Vineet K Raghu, Michael T Lu, Tomas G Neilan, Julius C Heemelaar

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Marcel C LangenbachCardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. mlangenbach@mgh.harvard.edu.ORCID http://orcid.org/0000-0002-8168-2714
Borek FoldynaCardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Ibrahim HadzicCardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Isabel L LangenbachCardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Vineet K RaghuCardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Michael T LuCardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Tomas G NeilanCardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Julius C HeemelaarCardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Funding

Deutsche Forschungsgemeinschaft 502109212
6 · The paper itself

Abstract

purposeMedical reports, governed by HIPAA regulations, contain personal health information (PHI), restricting secondary data use. Utilizing natural language processing (NLP) and large language models (LLM), we sought to employ publicly available methods to automatically anonymize PHI in free-text radiology reports. MATERIALS AND

methodsWe compared two publicly available rule-based NLP models (spaCy; NLP

resultsNLP

conclusionPre-trained NLP models can effectively anonymize free-text radiology reports, while anonymization with the LLM model is more prone to deleting medical information. KEY POINTS: Question This study compares NLP and locally hosted LLM techniques to ensure PHI anonymization without losing clinical information. Findings Pre-trained NLP models effectively anonymized radiology reports without removing clinical data, while a locally hosted LLM was less reliable, risking the loss of important information. Clinical relevance Fast, reliable, automated anonymization of PHI from radiology reports enables HIPAA-compliant secondary use, facilitating advanced applications like LLM-driven radiology analysis while ensuring ethical handling of sensitive patient data.

Indexed as

Data AnonymizationNatural Language ProcessingRadiology Information SystemsElectronic Health RecordsHumansLarge Language ModelsData anonymizationDiagnostic imagingElectronic health recordsMachine learningNatural language processing

Identifiers

PMID39480533

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.