Patient-centered radiology reports with generative artificial intelligence: adding value to radiology reporting
Open accessAbstract
The purposes were to assess the efficacy of AI-generated radiology reports in terms of report summary, patient-friendliness, and recommendations and to evaluate the consistent performance of report quality and accuracy, contributing to the advancement of radiology workflow. Total 685 spine MRI reports were retrieved from our hospital database. AI-generated radiology reports were generated in three formats: (1) summary reports, (2) patient-friendly reports, and (3) recommendations. The occurrence of artificial hallucinations was evaluated in the AI-generated reports. Two radiologists conducted qualitative and quantitative assessments considering the original report as a standard reference. Two non-physician raters assessed their understanding of the content of original and patient-friendly reports using a 5-point Likert scale. The scoring of the AI-generated radiology reports were overall high average scores across all three formats. The average comprehension score for the original report was 2.71 ± 0.73, while the score for the patient-friendly reports significantly increased to 4.69 ± 0.48 ( p < 0.001). There were 1.12% artificial hallucinations and 7.40% potentially harmful translations. In conclusion, the potential benefits of using generative AI assistants to generate these reports include improved report quality, greater efficiency in radiology workflow for producing summaries, patient-centered reports, and recommendations, and a move toward patient-centered radiology.
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Generative AI chatbots offer potential benefits such as enhanced report quality, improved efficiency, and creation of patient-centered radiology summaries and recommendations.
“Hence, generative AI chatbots offer potential benefits such as enhanced report quality, improved efficiency, and creation of patient-centered radiology summaries and recommendations [27].”
From Evaluating the Capability of Large Language Model Chatbots for Generating Plain Language Summaries in Radiology (2025) · cites this paper as [27]
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Cites
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works
Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., et al. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2023.1838 · Cited by 2,613 · cited 1× · see citing works
“Similar to a study 12 , instead of a succinct one-word response from a human doctor, or even if it is a recommendation that pinpoints a single key point, people would perceive a comprehensive explanation by ChatGPT as superior, and it could be indicative of the compassionate attributes associated…”
BackgroundChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports
Jeblick, K., Schachtner, B., Dexl, J., Mittermeier, A., Stüber, A. T., Topalis, J., Weber, T., Wesp, P., Sabel, B. O., Ricke, J., et al. (2023) · European Radiology · doi:10.1007/s00330-023-10213-1 · Cited by 549 · see citing works
Lyu, Q., Tan, J., Zapadka, M. E., Ponnatapura, J., Niu, C., Myers, K. J., Wang, G., & Whitlow, C. T. (2023) · Visual Computing for Industry, Biomedicine, and Art · doi:10.1186/s42492-023-00136-5 · Cited by 367 · see citing works
Adams, L. C., Truhn, D., Busch, F., Kader, A., Niehues, S. M., Makowski, M. R., & Bressem, K. K. (2023) · Radiology · doi:10.1148/radiol.230725 · Cited by 339 · see citing works
Readability of radiology reports: implications for patient-centered care
Martin-Carreras, T., Cook, T. S., & Kahn, C. E. (2019) · Clinical Imaging · doi:10.1016/j.clinimag.2018.12.006 · Cited by 87 · see citing works
Automatic Text Summarization of Biomedical Text Data: A Systematic Review
Chaves, A., Kesiku, C., & Garcia-Zapirain, B. (2022) · Information · doi:10.3390/info13080393 · Cited by 53 · see citing works
Flagged first, then by citations
Research works printed but not linked: 26
- [1]Rockall AG Justich C Helbich T Vilgrain V Patient communication in radiology: Moving up the agenda Eur. J. Radiol. 2022 155 110464 10.1016/j.ejrad.2022.110464 36038410 · DOI
- [2]Vincoff NS Barish MA Grimaldi G The patient-friendly radiology report: History, evolution, challenges and opportunities Clin. Imaging 2022 89 128 135 10.1016/j.clinimag.2022.06.018 35803159 · DOI
- [3]von Eckstaedt VHT Kitts AB Swanson C Hanley M Krishnaraj A Patient-centered radiology reporting for lung cancer screening J. Thorac. Imaging 2020 35 85 90 10.1097/RTI.0000000000000469 31913258 · DOI
- [5]Kadom N Safety-net academic hospital experience in following up noncritical yet potentially significant radiologist recommendations AJR Am. J. Roentgenol. 2017 209 982 986 10.2214/AJR.17.18179 28777651 · DOI
- [6]Kemp J Short R Bryant S Sample L Befera N Patient-friendly radiology reporting-implementation and outcomes J. Am. Coll. Radiol. 2022 19 377 383 10.1016/j.jacr.2021.10.008 35152963 · DOI
- [7]Henshaw D Access to radiology reports via an online patient portal: Experiences of referring physicians and patients J. Am. Coll. Radiol. 2015 12 582 586 10.1016/j.jacr.2015.01.015 26047399 · DOI
- [8]Miles RC Patient access to online radiology reports: Frequency and sociodemographic characteristics associated with use Acad. Radiol. 2016 23 1162 1169 10.1016/j.acra.2016.05.005 27287715 · DOI
- [9]Alarifi M Patrick T Jabour A Wu M Luo J Full radiology report through patient web portal: A literature review Int. J. Environ. Res. Public Health 2020 17 3673 10.3390/ijerph17103673 32456099 PMC7277373 · DOI
- [10]Li D Gupta K Chong J Evaluating diagnostic performance of ChatGPT in radiology: Delving into methods Radiology 2023 308 e232082 10.1148/radiol.232082 37724974 · DOI
- [11]Russe MF Performance of ChatGPT, human radiologists, and context-aware ChatGPT in identifying AO codes from radiology reports Sci. Rep. 2023 13 14215 10.1038/s41598-023-41512-8 37648742 PMC10468502 · DOI
- [13]Haug CJ Drazen JM Artificial intelligence and machine learning in clinical medicine, 2023 N. Engl. J. Med. 2023 388 1201 1208 10.1056/NEJMra2302038 36988595 · DOI · Paper page
- [14]Alkaissi H McFarlane SI Artificial hallucinations in ChatGPT: Implications in scientific writing Cureus 2023 15 e35179 10.7759/cureus.35179 36811129 PMC9939079 · DOI · Paper page
- [15]Shen Y ChatGPT and other large language models are double-edged swords Radiology 2023 307 e230163 10.1148/radiol.230163 36700838 · DOI · Paper page
- [16]Schmidt S Zimmerer A Cucos T Feucht M Navas L Simplifying radiologic reports with natural language processing: a novel approach using ChatGPT in enhancing patient understanding of MRI results Arch. Orthopaed. Trauma Surg. 2024 144 611 618 10.1007/s00402-023-05113-4 37950763 · DOI
- [17]Amin KS Accuracy of ChatGPT, Google Bard, and Microsoft Bing for simplifying radiology reports Radiology 2023 309 e232561 10.1148/radiol.232561 37987662 · DOI
- [21]Johnson AJ Improving the quality of radiology reporting: A physician survey to define the target J. Am. College Radiol. 2004 1 497 505 10.1016/j.jacr.2004.02.019 17411639 · DOI
- [22]Landis JR Koch GG The measurement of observer agreement for categorical data Biometrics 1977 33 159 174 10.2307/2529310 843571 · DOI · Paper page
- [24]Doyle C Lennox L Bell D A systematic review of evidence on the links between patient experience and clinical safety and effectiveness BMJ Open 2013 3 e001570 10.1136/bmjopen-2012-001570 23293244 PMC3549241 · DOI
- [25]Kim S Lee C-K Kim S-S Large language models: A guide for radiologists Korean J. Radiol. 2024 25 126 133 10.3348/kjr.2023.0997 38288895 PMC10831297 · DOI
- [26]Elkassem AA Smith AD Potential use cases for ChatGPT in radiology reporting AJR Am. J. Roentgenol. 2023 221 373 376 10.2214/AJR.23.29198 37095665 · DOI
- [27]Russe, M. F., Reisert, M., Bamberg, F. & Rau, A. Improving the use of LLMs in radiology through prompt engineering: from precision prompts to zero-shot learning. Rofo. 10.1055/a-2264-5631 (2024). 38408477 · DOI · Paper page
- [28]Bhayana R Chatbots and large language models in radiology: A practical primer for clinical and research applications Radiology 2024 310 e232756 10.1148/radiol.232756 38226883 · DOI
- [31]Yang X A large language model for electronic health records NPJ. Digit. Med. 2022 5 194 10.1038/s41746-022-00742-2 36572766 PMC9792464 · DOI · Paper page
- [33]Rahsepar AA Large language models for enhancing radiology report impressions: Improve readability while decreasing burnout Radiology 2024 310 e240498 10.1148/radiol.240498 38530179 · DOI
- [34]Doshi R Quantitative evaluation of large language models to streamline radiology report impressions: A multimodal retrospective analysis Radiology 2024 310 e231593 10.1148/radiol.231593 38530171 · DOI
- [35]Yi PH Golden SK Harringa JB Kliewer MA Readability of lumbar spine MRI reports: Will patients understand? AJR Am. J. Roentgenol. 2019 212 602 606 10.2214/AJR.18.20197 30620671 · DOI · Paper page
Cited by
Sorted by citations
PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
Zhang, X., Wu, C., Zhao, Z., Lin, W., Zhang, Y., Wang, Y., & Xie, W. (2023) · arXiv · doi:10.48550/arxiv.2305.10415 · Cited by 66 · see citing works
“The implementation details is provided in the supplementary materials.”
BackgroundAgentic LLM Workflows for Generating Patient-Friendly Medical Reports
Sudarshan, M., Shih, S., Yee, E., Yang, A., Zou, J., Chen, C., Zhou, Q., Chen, L., Singhal, C., & Shih, G. (2024) · arXiv · doi:10.48550/arxiv.2408.01112 · Cited by 8 · see citing works
“In applying generative artificial intelligence to the creation of patient-friendly reports, the pipeline can be made more efficient and patients can have access to more meaningful and legible letters [7, 8].”
BackgroundSarangi, P. K., Hui, P., Mondal, H., Kumar Nayak, D. S., Swarup, M. S., Ishan, & Panda, S. (2025) · iRADIOLOGY · doi:10.1002/ird3.70030 · Cited by 6 · see citing works
- Authors
- Park, J., Oh, K., Han, K., & Lee, Y. H.
- Affiliations
- Yonsei University (KR)
- Corresponding author
- Han, Kyunghwa
- Lee, Young Han
- Venue
- Scientific Reports
- Published
- 2024-06-08
- Volume
- 14
- Issue
- 1
- Pages
- 13218-13218
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11162416
- OpenAlex
- W4399458523
- Cited by
- 94
- References
- 7
- Citations by year
- 2026: 40 · 2025: 43 · 2024: 11
- FWCI
- 8.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Research Foundation of Korea (NRF-2022R1F1A1071702)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- generative AI, patient-centered care, spinal MRI, reporting quality, radiology workflow, clinical guidelines
- MeSH terms
- Adult, Aged, Artificial Intelligence, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Radiology, Patient-Centered Care, Workflow
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