Nobel JM, van Geel K, Robben SGF. Structured reporting in radiology: a systematic review to explore its potential. Eur Radiol. 2022;1–18.

Durack JC. The value proposition of structured reporting in interventional radiology. Am J Roentgenol. 2014. https://doi.org/10.2214/AJR.14.13112.

Article  Google Scholar 

Alarifi M, Jabour AM, Wu M, Aldosary A, Almanaa M, Luo J. Proposed questions to assess the extent of knowledge in understanding the radiology report language. Int J Environ Res Public Health. 2022;19:11808.

Article  PubMed  PubMed Central  Google Scholar 

Farmer CI, Bourne AM, O’Connor D, Jarvik JG, Buchbinder R. Enhancing clinician and patient understanding of radiology reports: a scoping review of international guidelines. Insights Imaging. 2020;11:1–10.

Article  Google Scholar 

Olthof AW, Leusveld ALM, de Groot JC, Callenbach PMC, van Ooijen PMA. Contextual structured reporting in radiology: implementation and long-term evaluation in improving the communication of critical findings. J Med Syst. 2020;44:148.

Article  PubMed  PubMed Central  Google Scholar 

Larson DB. Strategies for implementing a standardized structured radiology reporting program. Radiographics. 2018;38:1705–16.

Article  PubMed  Google Scholar 

Kemp J, Short R, Bryant S, Sample L, Befera N. Patient-friendly radiology reporting—implementation and outcomes. J Am Coll Radiol. 2022;19:377–83.

Article  PubMed  Google Scholar 

Sigl B, Herold C. Structured reporting in radiology—a chance for young radiologists? Radiologe. 2021;61:487–9.

Article  PubMed  PubMed Central  Google Scholar 

López-Úbeda P, Martín-Noguerol T, Luna A. Radiology in the era of large language models: the near and the dark side of the moon. Eur Radiol. 2023. https://doi.org/10.1007/s00330-023-09901-9.

Article  PubMed  Google Scholar 

López-Úbeda P, Martín-Noguerol T, Juluru K, Luna A. Natural language processing in radiology: update on clinical applications. J Am College Radiol. 2022. https://doi.org/10.1016/j.jacr.2022.06.016.

Article  Google Scholar 

Casey A, Davidson E, Poon M, Dong H, Duma D, Grivas A, et al. A systematic review of natural language processing applied to radiology reports. BMC Med Inform Decis Mak. 2021;21:179.

Article  PubMed  PubMed Central  Google Scholar 

H Touvron, L Martin, K Stone, P Albert, A Almahairi, Y Babaei, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:230709288. 2023;

T Dettmers, A Pagnoni, A Holtzman, L Zettlemoyer. QLoRA: Efficient Finetuning of Quantized LLMs. 2023.

K Papineni, S Roukos, T Ward, W-J Zhu. Bleu: a method for automatic evaluation of machine translation. Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 2002. p. 311–8.

S Banerjee, A Lavie. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization. 2005. p. 65–72.

C-Y Lin. Rouge: A package for automatic evaluation of summaries. Text summarization branches out. 2004. p. 74–81.

R Vedantam, C Lawrence Zitnick, D Parikh. Cider: Consensus-based image description evaluation. Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. p. 4566–75.

Jungmann F, Arnhold G, Kämpgen B, Jorg T, Düber C, Mildenberger P, et al. A hybrid reporting platform for extended RadLex coding combining structured reporting templates and natural language processing. J Digit Imaging. 2020;33:1026–33.

Article  PubMed  PubMed Central  Google Scholar 

Fanni SC, Romei C, Ferrando G, Volpi F, D’Amore CA, Bedini C, et al. Natural language processing to convert unstructured COVID-19 chest-CT reports into structured reports. Eur J Radiol Open. 2023;11:100512.

Article  PubMed  PubMed Central  Google Scholar 

Jorg T, Kämpgen B, Feiler D, Müller L, Düber C, Mildenberger P, et al. Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing. Insights Imaging. 2023;14:47.

Article  PubMed  PubMed Central  Google Scholar 

D’Antonoli TA, Stanzione A, Bluethgen C, Vernuccio F, Ugga L, Klontzas ME, et al. 2023. Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions. Diagn Interv Radiol.

Hasani AM, Singh S, Zahergivar A, Ryan B, Nethala D, Bravomontenegro G, et al. Evaluating the performance of Generative Pre-trained Transformer-4 (GPT-4) in standardizing radiology reports. Eur Radiol. 2023. https://doi.org/10.1007/s00330-023-10384-x.

Article  PubMed  Google Scholar 

Liu Z, Li Y, Shu P, Zhong A, Yang L, Ju C, et al. Radiology-Llama2: Best-in-Class Large Language Model for Radiology. 2023.

Tariq A, Urooj A, Trivedi S, Fathizadeh S, Ramasamy G, Tan N, et al. Patient centric summarization of radiology findings using large language models. medRxiv. 2024;54:116.

Google Scholar 

Sun S, Lupton K, Batch K, Nguyen H, Gazit L, Gangai N, et al. Natural language processing of large-scale structured radiology reports to identify oncologic patients with or without splenomegaly over a 10-year period. JCO Clin Cancer Inform. 2022;6:e2100104.

Article  PubMed  PubMed Central  Google Scholar 

Vosshenrich J, Nesic I, Boll DT, Heye T. Investigating the impact of structured reporting on the linguistic standardization of radiology reports through natural language processing over a 10-year period. Eur Radiol. 2023;33:7496–506.

Article  PubMed  PubMed Central  Google Scholar 

Do RKG, Lupton K, Causa Andrieu PI, Luthra A, Taya M, Batch K, et al. Patterns of metastatic disease in patients with cancer derived from natural language processing of structured CT radiology reports over a 10-year period. Radiology. 2021;301:115–22.

Article  PubMed  Google Scholar 

Fink MA, Kades K, Bischoff A, Moll M, Schnell M, Küchler M, et al. Deep learning–based assessment of oncologic outcomes from natural language processing of structured radiology reports. Radiol Artif Intell. 2022;4:e220055.

Article  PubMed  PubMed Central  Google Scholar 

Donnelly LF, Grzeszczuk R, Guimaraes CV, Zhang W, Bisset GS III. Using a natural language processing and machine learning algorithm program to analyze inter-radiologist report style variation and compare variation between radiologists when using highly structured versus more free text reporting. Curr Probl Diagn Radiol. 2019;48:524–30.

Article  PubMed  Google Scholar 

GaneshanD,Duong P-AT, Probyn L, Lenchik L, McArthur TA, Retrouvey M, et al. Structured reporting in radiology. Acad Radiol. 2018;25:66–73.

Article  Google Scholar 

Weiss DL, Langlotz CP. Structured reporting: patient care enhancement or productivity nightmare? Radiology. 2008;249:739–47.

Article  PubMed  Google Scholar 

Yates A, Dempsey PJ, Vencken S, MacMahon PJ, Hutchinson BD. Structured reporting in portable chest radiographs: An essential tool in the diagnosis of COVID-19. Eur J Radiol. 2021;134:109414.

Article  PubMed  Google Scholar 

European Society of Radiology (ESR). Communications@ myesr. org ES ESR paper on structured reporting in radiology. Insights Imaging. 2018;9:1–7.

Article  Google Scholar 

Lin E, Powell DK, Kagetsu NJ. Efficacy of a checklist-style structured radiology reporting template in reducing resident misses on cervical spine computed tomography examinations. J Digit Imaging. 2014;27:588–93.

Article  PubMed  PubMed Central  Google Scholar