Structured reporting in radiology: Transforming radiologists from image describers to clinical decision enablers_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

 HUANG Zixing

Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

HUANG Zixing, Email: zixinghuang@hotmail.com

Keywords：

department of radiology; imaging diagnosis; structured reporting; medical informatics; artificial intelligence

DOI：

10.7507/1007-9424.202504041

Video：

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The radiology diagnosis report encapsulates radiologists’ comprehensive analytical insights and deep interpretive understanding of patients’ imaging data, serving as an essential basis for disease diagnosis, clinical treatment planning, and prognosis assessment. As the primary medium through which radiologists contribute substantively to patient’s care, traditional free-text reports represent subjective interpretations shaped by individual experience and stylistic preferences. Such reliance on personal factors can introduce inconsistencies and limitations in clinical applications. To address these challenges, structured radiology reporting has been developed. We present a concise overview of the origins, developmental trajectory, current landscape, and emerging trends of structured radiology reports, highlighting their role in advancing standardized.

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2.	Mityul MI, Gilcrease-Garcia B, Mangano MD, et al. Radiology reporting: Current practices and an introduction to patient-centered opportunities for improvement. AJR Am J Roentgenol, 2018, 210(2): 376-385.
3.	van Ooijen P, Ranschaert ER, Trianni A, et al. Imaging informatics for healthcare professionals // Structured reporting in radiology. New York: Springer, 2022.
4.	Teh SR, Ranguis S, Fagan P. Inter-observer variability between radiologists reporting on cerebellopontine angle tumours on magnetic resonance imaging. J Laryngol Otol, 2017, 131(S1): S47-S49. doi: 10.1017/S002221511600935X.
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6.	Shinagare AB, Lacson R, Boland GW, et al. Radiologist preferences, agreement, and variability in phrases used to convey diagnostic certainty in radiology reports. J Am Coll Radiol, 2019, 16(4 Pt A): 458-464.
7.	Madan SS, Rai A, Harley JM. Interobserver error in interpretation of the radiographs for degeneration of the lumbar spine. Iowa Orthop J, 2003, 23: 51-56.
8.	Gunn AJ, Tuttle MC, Flores EJ, et al. Differing interpretations of report terminology between primary care physicians and radiologists. J Am Coll Radiol, 2016, 13(12 Pt A): 1525-1529.
9.	Khorasani R, Bates DW, Teeger S, et al. Is terminology used effectively to convey diagnostic certainty in radiology reports?. Acad Radiol, 2003, 10(6): 685-688.
10.	European Society of Radiology (ESR). ESR paper on structured reporting in radiology. Insights Imaging, 2018, 9(1): 1-7.
11.	Weiss DL, Bolos PR. Reporting and dictation // Branstetter B. Practical imaging informatics. New York: Springer, 2009.
12.	Persigehl T, Baumhauer M, Baeßler B, et al. Structured reporting of solid and cystic pancreatic lesions in CT and MRI: Consensus-based structured report templates of the German Society of Radiology (DRG). Rofo, 2020, 192(7): 641-656.
13.	Neri E, Granata V, Montemezzi S, et al. Structured reporting of X-ray mammography in the first diagnosis of breast cancer: a Delphi consensus proposal. Radiol Med, 2022, 127(5): 471-483.
14.	Granata V, Morana G, D’Onofrio M, et al. Structured reporting of computed tomography and magnetic resonance in the staging of pancreatic adenocarcinoma: A Delphi consensus proposal. Diagnostics (Basel), 2021, 11(11): 2033. doi: 10.3390/diagnostics11112033.
15.	Granata V, Faggioni L, Grassi R, et al. Structured reporting of computed tomography in the staging of colon cancer: a Delphi consensus proposal. Radiol Med, 2022, 127(1): 21-29.
16.	中国医师协会结直肠肿瘤专业委员会诊疗技术专委会, 中华医学会放射学分会腹部学组. 直肠癌MR扫描及结构式报告规范专家共识. 中华放射学杂志, 2021, 55(11): 1121-1127.
17.	Elmohr MM, Chernyak V, Sirlin CB, et al. Liver imaging reporting and data system comprehensive guide: MR imaging edition. Magn Reson Imaging Clin N Am, 2021, 29(3): 375-387.
18.	Olthof AW, Leusveld ALM, de Groot JC, et al. Contextual structured reporting in radiology: Implementation and long-term evaluation in improving the communication of critical findings. J Med Syst, 2020, 44(9): 148. doi: 10.1007/s10916-020-01609-3.
19.	Pinto Dos Santos D, Kotter E. Structured radiology reporting on an institutional level-benefit or new administrative burden?. Ann N Y Acad Sci, 2018, 1434(1): 274-281.
20.	European Society of Radiology (ESR). ESR paper on structured reporting in radiology-update 2023. Insights Imaging, 2023, 14(1): 199. doi: 10.1186/s13244-023-01560-0.
21.	Chen JY, Sippel Schmidt TM, Carr CD, et al. Enabling the next-generation radiology report: Description of two new system standards. Radiographics, 2017, 37(7): 2106-2112.
22.	Fanni SC, Romei C, Ferrando G, et al. Natural language processing to convert unstructured COVID-19 chest-CT reports into structured reports. Eur J Radiol Open, 2023, 11: 100512. doi: 10.1016/j.ejro.2023.100512.
23.	Adams LC, Truhn D, Busch F, et al. Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: A multilingual feasibility study. Radiology, 2023, 307(4): e230725. doi: 10.1148/radiol.230725.
24.	Busch F, Hoffmann L, Dos Santos DP, et al. Large language models for structured reporting in radiology: past, present, and future. Eur Radiol, 2024 Oct 23. doi: 10.1007/s00330-024-11107-6.
25.	Butler JJ, Acosta E, Kuna MC, et al. Decoding radiology reports: Artificial intelligence-large language models can improve the readability of hand and wrist orthopedic radiology reports. Hand (NY), 2024 Aug 13: 15589447241267766. doi: 10.1177/15589447241267766.
26.	Mallio CA, Sertorio AC, Bernetti C, et al. Large language models for structured reporting in radiology: performance of GPT-4, ChatGPT-3. 5, perplexity and bing. Radiol Med, 2023, 128(7): 808-812.
27.	Bhayana R, Nanda B, Dehkharghanian T, et al. Large language models for automated synoptic reports and resectability categorization in pancreatic cancer. Radiology, 2024, 311(3): e233117. doi: 10.1148/radiol.233117.
28.	Rao VM, Hla M, Moor M, et al. Multimodal generative AI for medical image interpretation. Nature, 2025, 639(8056): 888-896.
29.	Tanno R, Barrett DGT, Sellergren A, et al. Collaboration between clinicians and vision-language models in radiology report generation. Nat Med, 2025, 31(2): 599-608.
30.	Amin K, Khosla P, Doshi R, et al. Artificial intelligence to improve patient understanding of radiology reports. Yale J Biol Med, 2023, 96(3): 407-417.
31.	Mityul MI, Gilcrease-Garcia B, Searleman A, et al. Interpretive differences between patients and radiologists regarding the diagnostic confidence associated with commonly used phrases in the radiology report. AJR Am J Roentgenol, 2018, 210(1): 123-126.
32.	Park J, Oh K, Han K, et al. Patient-centered radiology reports with generative artificial intelligence: adding value to radiology reporting. Sci Rep, 2024, 14(1): 13218. doi: 10.1038/s41598-024-63824-z.
33.	Maroncelli R, Rizzo V, Pasculli M, et al. Probing clarity: AI-generated simplified breast imaging reports for enhanced patient comprehension powered by ChatGPT-4o. Eur Radiol Exp, 2024, 8(1): 124. doi: 10.1186/s41747-024-00526-1.
34.	De-Giorgio F, Benedetti B, Mancino M, et al. The need for balancing ‘black box’ systems and explainable artificial intelligence: A necessary implementation in radiology. Eur J Radiol, 2025, 185: 112014. doi: 10.1016/j.ejrad.2025.112014.
35.	Bhadra S, Kelkar VA, Brooks FJ, et al. On hallucinations in tomographic image reconstruction. IEEE Trans Med Imaging, 2021, 40(11): 3249-3260.
36.	Dumit J, Roepstorff A. AI hallucinations are a feature of LLM design, not a bug. Nature, 2025, 639(8053): 38. doi: 10.1038/d41586-025-00662-7.
37.	Jorg T, Halfmann MC, Arnhold G, et al. Implementation of structured reporting in clinical routine: a review of 7 years of institutional experience. Insights Imaging, 2023, 14(1): 61. doi: 10.1186/s13244-023-01408-7.
38.	O’Reilly PA, Lewis S, Reed W. Assessing the implementation of COVID-19 structured reporting templates for chest radiography: a scoping review. BJR Open, 2023, 5(1): 20220058. doi: 10.1259/bjro.20220058.

1. Hartung MP, Bickle IC, Gaillard F, et al. How to create a great radiology report. Radiographics, 2020, 40(6): 1658-1670.
2. Mityul MI, Gilcrease-Garcia B, Mangano MD, et al. Radiology reporting: Current practices and an introduction to patient-centered opportunities for improvement. AJR Am J Roentgenol, 2018, 210(2): 376-385.
3. van Ooijen P, Ranschaert ER, Trianni A, et al. Imaging informatics for healthcare professionals // Structured reporting in radiology. New York: Springer, 2022.
4. Teh SR, Ranguis S, Fagan P. Inter-observer variability between radiologists reporting on cerebellopontine angle tumours on magnetic resonance imaging. J Laryngol Otol, 2017, 131(S1): S47-S49. doi: 10.1017/S002221511600935X.
5. Quinn L, Tryposkiadis K, Deeks J, et al. Interobserver variability studies in diagnostic imaging: a methodological systematic review. Br J Radiol, 2023, 96(1148): 20220972. doi: 10.1259/bjr.20220972.
6. Shinagare AB, Lacson R, Boland GW, et al. Radiologist preferences, agreement, and variability in phrases used to convey diagnostic certainty in radiology reports. J Am Coll Radiol, 2019, 16(4 Pt A): 458-464.
7. Madan SS, Rai A, Harley JM. Interobserver error in interpretation of the radiographs for degeneration of the lumbar spine. Iowa Orthop J, 2003, 23: 51-56.
8. Gunn AJ, Tuttle MC, Flores EJ, et al. Differing interpretations of report terminology between primary care physicians and radiologists. J Am Coll Radiol, 2016, 13(12 Pt A): 1525-1529.
9. Khorasani R, Bates DW, Teeger S, et al. Is terminology used effectively to convey diagnostic certainty in radiology reports?. Acad Radiol, 2003, 10(6): 685-688.
10. European Society of Radiology (ESR). ESR paper on structured reporting in radiology. Insights Imaging, 2018, 9(1): 1-7.
11. Weiss DL, Bolos PR. Reporting and dictation // Branstetter B. Practical imaging informatics. New York: Springer, 2009.
12. Persigehl T, Baumhauer M, Baeßler B, et al. Structured reporting of solid and cystic pancreatic lesions in CT and MRI: Consensus-based structured report templates of the German Society of Radiology (DRG). Rofo, 2020, 192(7): 641-656.
13. Neri E, Granata V, Montemezzi S, et al. Structured reporting of X-ray mammography in the first diagnosis of breast cancer: a Delphi consensus proposal. Radiol Med, 2022, 127(5): 471-483.
14. Granata V, Morana G, D’Onofrio M, et al. Structured reporting of computed tomography and magnetic resonance in the staging of pancreatic adenocarcinoma: A Delphi consensus proposal. Diagnostics (Basel), 2021, 11(11): 2033. doi: 10.3390/diagnostics11112033.
15. Granata V, Faggioni L, Grassi R, et al. Structured reporting of computed tomography in the staging of colon cancer: a Delphi consensus proposal. Radiol Med, 2022, 127(1): 21-29.
16. 中国医师协会结直肠肿瘤专业委员会诊疗技术专委会, 中华医学会放射学分会腹部学组. 直肠癌MR扫描及结构式报告规范专家共识. 中华放射学杂志, 2021, 55(11): 1121-1127.
17. Elmohr MM, Chernyak V, Sirlin CB, et al. Liver imaging reporting and data system comprehensive guide: MR imaging edition. Magn Reson Imaging Clin N Am, 2021, 29(3): 375-387.
18. Olthof AW, Leusveld ALM, de Groot JC, et al. Contextual structured reporting in radiology: Implementation and long-term evaluation in improving the communication of critical findings. J Med Syst, 2020, 44(9): 148. doi: 10.1007/s10916-020-01609-3.
19. Pinto Dos Santos D, Kotter E. Structured radiology reporting on an institutional level-benefit or new administrative burden?. Ann N Y Acad Sci, 2018, 1434(1): 274-281.
20. European Society of Radiology (ESR). ESR paper on structured reporting in radiology-update 2023. Insights Imaging, 2023, 14(1): 199. doi: 10.1186/s13244-023-01560-0.
21. Chen JY, Sippel Schmidt TM, Carr CD, et al. Enabling the next-generation radiology report: Description of two new system standards. Radiographics, 2017, 37(7): 2106-2112.
22. Fanni SC, Romei C, Ferrando G, et al. Natural language processing to convert unstructured COVID-19 chest-CT reports into structured reports. Eur J Radiol Open, 2023, 11: 100512. doi: 10.1016/j.ejro.2023.100512.
23. Adams LC, Truhn D, Busch F, et al. Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: A multilingual feasibility study. Radiology, 2023, 307(4): e230725. doi: 10.1148/radiol.230725.
24. Busch F, Hoffmann L, Dos Santos DP, et al. Large language models for structured reporting in radiology: past, present, and future. Eur Radiol, 2024 Oct 23. doi: 10.1007/s00330-024-11107-6.
25. Butler JJ, Acosta E, Kuna MC, et al. Decoding radiology reports: Artificial intelligence-large language models can improve the readability of hand and wrist orthopedic radiology reports. Hand (NY), 2024 Aug 13: 15589447241267766. doi: 10.1177/15589447241267766.
26. Mallio CA, Sertorio AC, Bernetti C, et al. Large language models for structured reporting in radiology: performance of GPT-4, ChatGPT-3. 5, perplexity and bing. Radiol Med, 2023, 128(7): 808-812.
27. Bhayana R, Nanda B, Dehkharghanian T, et al. Large language models for automated synoptic reports and resectability categorization in pancreatic cancer. Radiology, 2024, 311(3): e233117. doi: 10.1148/radiol.233117.
28. Rao VM, Hla M, Moor M, et al. Multimodal generative AI for medical image interpretation. Nature, 2025, 639(8056): 888-896.
29. Tanno R, Barrett DGT, Sellergren A, et al. Collaboration between clinicians and vision-language models in radiology report generation. Nat Med, 2025, 31(2): 599-608.
30. Amin K, Khosla P, Doshi R, et al. Artificial intelligence to improve patient understanding of radiology reports. Yale J Biol Med, 2023, 96(3): 407-417.
31. Mityul MI, Gilcrease-Garcia B, Searleman A, et al. Interpretive differences between patients and radiologists regarding the diagnostic confidence associated with commonly used phrases in the radiology report. AJR Am J Roentgenol, 2018, 210(1): 123-126.
32. Park J, Oh K, Han K, et al. Patient-centered radiology reports with generative artificial intelligence: adding value to radiology reporting. Sci Rep, 2024, 14(1): 13218. doi: 10.1038/s41598-024-63824-z.
33. Maroncelli R, Rizzo V, Pasculli M, et al. Probing clarity: AI-generated simplified breast imaging reports for enhanced patient comprehension powered by ChatGPT-4o. Eur Radiol Exp, 2024, 8(1): 124. doi: 10.1186/s41747-024-00526-1.
34. De-Giorgio F, Benedetti B, Mancino M, et al. The need for balancing ‘black box’ systems and explainable artificial intelligence: A necessary implementation in radiology. Eur J Radiol, 2025, 185: 112014. doi: 10.1016/j.ejrad.2025.112014.
35. Bhadra S, Kelkar VA, Brooks FJ, et al. On hallucinations in tomographic image reconstruction. IEEE Trans Med Imaging, 2021, 40(11): 3249-3260.
36. Dumit J, Roepstorff A. AI hallucinations are a feature of LLM design, not a bug. Nature, 2025, 639(8053): 38. doi: 10.1038/d41586-025-00662-7.
37. Jorg T, Halfmann MC, Arnhold G, et al. Implementation of structured reporting in clinical routine: a review of 7 years of institutional experience. Insights Imaging, 2023, 14(1): 61. doi: 10.1186/s13244-023-01408-7.
38. O’Reilly PA, Lewis S, Reed W. Assessing the implementation of COVID-19 structured reporting templates for chest radiography: a scoping review. BJR Open, 2023, 5(1): 20220058. doi: 10.1259/bjro.20220058.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Latest ArticlesStructured reporting in radiology: Transforming radiologists from image describers to clinical decision enablers

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