Preparing a collection of radiology examinations for distribution and retrieval
Open accessAbstract
Objective Clinical documents made available for secondary use play an increasingly important role in discovery of clinical knowledge, development of research methods, and education. An important step in facilitating secondary use of clinical document collections is easy access to descriptions and samples that represent the content of the collections. This paper presents an approach to developing a collection of radiology examinations, including both the images and radiologist narrative reports, and making them publicly available in a searchable database. Materials and Methods The authors collected 3996 radiology reports from the Indiana Network for Patient Care and 8121 associated images from the hospitals’ picture archiving systems. The images and reports were de-identified automatically and then the automatic de-identification was manually verified. The authors coded the key findings of the reports and empirically assessed the benefits of manual coding on retrieval. Results The automatic de-identification of the narrative was aggressive and achieved 100% precision at the cost of rendering a few findings uninterpretable. Automatic de-identification of images was not quite as perfect. Images for two of 3996 patients (0.05%) showed protected health information. Manual encoding of findings improved retrieval precision. Conclusion Stringent de-identification methods can remove all identifiers from text radiology reports. DICOM de-identification of images does not remove all identifying information and needs special attention to images scanned from film. Adding manual coding to the radiologist narrative reports significantly improved relevancy of the retrieved clinical documents. The de-identified Indiana chest X-ray collection is available for searching and downloading from the National Library of Medicine ( http://openi.nlm.nih.gov/ ).
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Multiple benchmarks have been proposed to evaluate model summarisation capabilities in the biomedical domain, including over biomedical literature, medical forum conversations, and radiology reports.
“Multiple benchmarks have been proposed to evaluate model summarisation capabilities in the biomedical domain, including over biomedical literature37–40, medical forum conversations41, and radiology reports22,42.”
From A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation (2025) · cites this paper as [22]
The Abstract describes a publicly available collection of radiology images and narrative reports, so the source supports the radiology-report part of the citation. Its Retrieval Experiments and Conclusions evaluate search and retrieval, not model summarisation; our copy does not establish that the collection was proposed as a summarisation benchmark. The indexed passages provide section labels but no page numbers. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThere is increased usage of LLMs for clinical documentation.
“Given the increased usage of LLMs for clinical documentation22,23”
From A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation (2025) · cites this paper as [22]
The Abstract describes preparing radiology reports and images for public retrieval, while the Annotation of Radiology Reports section describes manual coding and use of the Medical Text Indexer (page numbers unavailable). Searches for LLMs, large language models, and their use in writing clinical documentation found no passage addressing increased LLM usage. (LLM, conf=0.76)
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- Authors
- Demner-Fushman, D., Kohli, M. D., Rosenman, M. B., Shooshan, S. E., Rodriguez, L., Antani, S., Thoma, G. R., & McDonald, C. J.
- Affiliations
- Staff Scientist, Lister Hill National Center for Biomedical Communications National Library of Medicine, National Institutes of Health Bldg. 38A, Room 10S-1022, 8600 Rockville Pike MSC-3824 Bethesda, MD 20894, USA
- Indiana University Indianapolis (US)
- National Institutes of Health (US)
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-07-01
- Volume
- 23
- Issue
- 2
- Pages
- 304-310
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC5009925
- OpenAlex
- W2152772232
- Cited by
- 1,164
- References
- 22
- Citations by year
- 2026: 191 · 2025: 264 · 2024: 194 · 2023: 154 · 2022: 98 · 2021: 97 · 2020: 68 · 2019: 58 · 2018: 23 · 2017: 12 · 2016: 4 · 2015: 1
- FWCI
- 25.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- IUScholarWorks (Indiana University) (submittedVersion)
- Purdue University Indianapolis (Indiana University) (submittedVersion)
- Funders
- National Institutes of Health
- U.S. National Library of Medicine
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Keywords
- radiology reports, de-identification, protected health information, PACS
- MeSH terms
- Data Anonymization, Humans, Radiology Information Systems, Radiography, Thoracic, Information Storage and Retrieval
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