ArticleInsights into imaging2023
Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 33 citations in OpenAlex.
- What do clinicians edit in ambient AI-drafted clinical documentation? A qualitative content analysis.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Assessing the Reliability, Accuracy, and Relevance of Artificial Intelligence Speech Recognition for Clinical Documentation: A Scoping Review.Journal of evaluation in clinical practice · 2026Article
- Article
- Structured vs. unstructured MRI reporting for lumbar spine degenerative conditions: a comparative evaluation using a custom radiology template.Skeletal radiology · 2026Article
- Application of Artificial Intelligence in Education of Prosthodontics and Implant Dentistry: A Review.Advances in medical education and practice · 2026Review
- Comprehensive review of pulmonary embolism imaging: past, present and future innovations in computed tomography (CT) and other diagnostic techniques.Japanese journal of radiology · 2025Review
- Technical requirements and optimization strategies for home-based teleradiology workstations: a review article.Insights into imaging · 2025Review
- Automated MRI pituitary structured reporting from free-text using a fine-tuned Llama model: a feasibility study.Japanese journal of radiology · 2025Article
- Cutting Edge to Cutting Time: Can ChatGPT Improve the Radiologist's Reporting?Journal of imaging informatics in medicine · 2025Article
- Evaluation of guided reporting: quality and reading time of automatically generated radiology report in breast magnetic resonance imaging using a dedicated software solution.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Academic Pursuits in Teleradiology: At the Crossroads of Technology, Radiology, and Implementation.The Indian journal of radiology & imaging · 2025Article
- American College of Veterinary Radiology and European College of Veterinary Diagnostic Imaging consensus statement on imaging report foundations.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2025Article
- Natural language processing-based analysis of the level of adoption by expert radiologists of the ASSR, ASNR and NASS version 2.0 of lumbar disc nomenclature: an eight-year survey.Quantitative imaging in medicine and surgery · 2024Article
- A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports.Insights into imaging · 2024Article
- Natural language processing to convert unstructured COVID-19 chest-CT reports into structured reports.European journal of radiology open · 2023Article
- ESR paper on structured reporting in radiology-update 2023.Insights into imaging · 2023Article
- Advancements in Standardizing Radiological Reports: A Comprehensive Review.Medicina (Kaunas, Lithuania) · 2023Review
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundStructured reporting (SR) is recommended in radiology, due to its advantages over free-text reporting (FTR). However, SR use is hindered by insufficient integration of speech recognition, which is well accepted among radiologists and commonly used for unstructured FTR. SR templates must be laboriously completed using a mouse and keyboard, which may explain why SR use remains limited in clinical routine, despite its advantages. Artificial intelligence and related fields, like natural language processing (NLP), offer enormous possibilities to facilitate the imaging workflow. Here, we aimed to use the potential of NLP to combine the advantages of SR and speech recognition.
resultsWe developed a reporting tool that uses NLP to automatically convert dictated free text into a structured report. The tool comprises a task-oriented dialogue system, which assists the radiologist by sending visual feedback if relevant findings are missed. The system was developed on top of several NLP components and speech recognition. It extracts structured content from dictated free text and uses it to complete an SR template in RadLex terms, which is displayed in its user interface. The tool was evaluated for reporting of urolithiasis CTs, as a use case. It was tested using fictitious text samples about urolithiasis, and 50 original reports of CTs from patients with urolithiasis. The NLP recognition worked well for both, with an F1 score of 0.98 (precision: 0.99; recall: 0.96) for the test with fictitious samples and an F1 score of 0.90 (precision: 0.96; recall: 0.83) for the test with original reports.
conclusionDue to its unique ability to integrate speech into SR, this novel tool could represent a major contribution to the future of reporting.
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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.