Interpretive radiology reports for lung cancer generated by GPT-4 large language model to enhance doctor–patient communication efficiency_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

YANG Xiongwen ¹ ,  HUANG Jian ²

1. Department of Thoracic Surgery, Guizhou Provincial People’s Hospital, Guiyang, 550002, P. R. China;
2. Department of Thoracic Surgery, Jiangxi Provincial Cancer Hospital, Nanchang, 330006, P. R. China;

Corresponding?author：

HUANG Jian, Email: szlhuangjian0023@163.com

Keywords：

GPT-4; large language model; lung cancer; radiology reports; doctor-patient communication

DOI：

10.7507/1007-4848.202510007

Video：

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Objective To explore the application of the GPT-4 large language model in simplifying lung cancer radiology reports to enhance patient comprehension and doctor–patient communication efficiency. Methods A total of 362 radiology reports of non-small cell lung cancer (NSCLC) patients were collected from two hospitals between September and December 2024. Interpretive radiology reports (IRRs) were generated using GPT-4. Original reports (ORRs) and IRRs were compared through radiologist consistency evaluation and volunteer-based assessments of reading time, comprehension scores, and simulated communication duration. Results The average word count of ORRs was 459.83±55.76, compared with 625.42±41.59 for IRRs (P<0.001). No significant differences were observed in expert consistency scores between ORRs and IRRs across dimensions of image interpretation accuracy, report detail completeness, explanatory depth and insight, and clinical practicality. Volunteers read IRRs more quickly with (346.88±29.15) s versus (409.01±102.40) s, achieved higher comprehension scores at (7.83±1.04) point versus (5.53±0.94) points, and required shorter communication times at (317.31±57.81) s versus (714.20±56.67) s with doctors. All differences were statistically significant (all P<0.001). Conclusion GPT-4 generated IRRs significantly improved patient comprehension and reduced communication time while maintaining medical accuracy. These findings suggest a new approach to optimizing radiology report management and enhancing healthcare service quality.

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1. Siegel RL, Kratzer TB, Giaquinto AN, et al. Cancer statistics, 2025. CA Cancer J Clin, 2025, 75(1): 10-45.
2. Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent, 2024, 4(1): 47-53.
3. Adams SJ, Stone E, Baldwin DR, et al. Lung cancer screening. Lancet, 2023, 401(10374): 390-408.
4. Truhn D, Loeffler CM, Muller-Franzes G, et al. Extracting structured information from unstructured histopathology reports using generative pre-trained transformer 4 (GPT-4). J Pathol, 2024, 262(3): 310-319.
5. Hartung MP, Bickle IC, Gaillard F, et al. How to create a great radiology report. Radiographics, 2020, 40(6): 1658-1670.
6. Haskard Zolnierek KB, DiMatteo MR. Physician communication and patient adherence to treatment: a meta-analysis. Med Care, 2009, 47(8): 826-834.
7. Oben P. Understanding the patient experience: a conceptual framework. J Patient Exp, 2020, 7(6): 906-910.
8. McDonald HP, Garg AX, Haynes RB. Interventions to enhance patient adherence to medication prescriptions: scientific review. JAMA, 2002, 288(22): 2868-2879.
9. Thirunavukarasu AJ, Ting DSJ, Elangovan K, et al. Large language models in medicine. Nat Med, 2023, 29(8): 1930-1940.
10. Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. Nature, 2023, 620(7972): 172-180.
11. Yang X, Zhang Y, Jiang J, et al. Harnessing GPT-4 for automated error detection in pathology reports: implications for oncology diagnostics. Digit Health, 2025, 11: 20552076251346703.
12. Kucukkaya A, Aktas Bajalan E, Moons P, et al. Equality, diversity, and inclusion in artificial intelligence-driven healthcare chatbots: addressing challenges and shaping strategies. Eur J Cardiovasc Nurs, 2025, 24(7): 1175-1181.
13. Zhi Z, Zhao J, Li Q, et al. Evolving perceptions and attitudes to adopting generative AI in professional settings: multicenter longitudinal qualitative study of senior Chinese hospital leaders. J Med Internet Res, 2025, 27: e75531.
14. Yang X, Xiao Y, Liu D, et al. Enhancing physician-patient communication in oncology using GPT-4 through simplified radiology reports: multicenter quantitative study. J Med Internet Res, 2025, 27: e63786.
15. Yang X, Xiao Y, Liu D, et al. Enhancing doctor-patient communication using large language models for pathology report interpretation. BMC Med Inform Decis Mak, 2025, 25(1): 36.
16. Prucker P, Bressem KK, Peeken J, et al. A prospective controlled trial of large language model-based simplification of oncologic CT reports for patients with cancer. Radiol, 2025, 317(2): e251844.
17. Yang X, Xiao Y, Liu D, et al. Cross-language transformation of free text into structured lobectomy surgical records from a multicenter study. Sci Rep, 2025, 15(1): 15417.
18. Sun Z, Ong H, Kennedy P, et al. Evaluating GPT-4 on impressions generation in radiology reports. Radiol, 2023, 307(5): e231259.
19. Ullah E, Parwani A, Baig MM, et al. Challenges and barriers of using large language models (LLM) such as ChatGPT for diagnostic medicine with a focus on digital pathology: a recent scoping review. Diagn Pathol, 2024, 19(1): 43.
20. Liu TL, Hetherington TC, Stephens C, et al. AI-powered clinical documentation and clinicians’ electronic health record experience: a nonrandomized clinical trial. JAMA Netw Open, 2024, 7(9): e2432460.
21. 周小芹, 劉慧珍, 王婷, 等. 基于大語言模型的臨床預測模型研究報告指南(TRIPOD-LLM)解讀. 中國胸心血管外科臨床雜志, 2025, 32(7): 940-946.Zhou XQ, Liu HZ, Wang T, et al. Interpretation of the TRIPOD-LLM reporting guideline for studies using large language models. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(7): 940-946.
22. Bhayana R. Chatbots and large language models in radiology: a practical primer for clinical and research applications. Radiol, 2024, 310(1): e232756.
23. Shah NH, Entwistle D, Pfeffer MA. Creation and adoption of large language models in medicine. JAMA, 2023, 330(9): 866-869.
24. Amin KS, Davis MA, Doshi R, et al. Accuracy of ChatGPT, google bard, and microsoft bing for simplifying radiology reports. Radiol, 2023, 309(2): e232561.
25. Zhou Y, Ong H, Kennedy P, et al. Evaluating GPT-4V (GPT-4 with vision) on detection of radiologic findings on chest radiographs. Radiol, 2024, 311(2): e233270.
26. Gertz RJ, Dratsch T, Bunck AC, et al. Potential of GPT-4 for detecting errors in radiology reports: implications for reporting accuracy. Radiol, 2024, 311(1): e232714.
27. Wang S, Zhu Y, Liu H, et al. Knowledge editing for large language models: a survey. ACM Comput Surv, 2024, 57(3): 1-37.
28. Ampel B, Yang CH, Hu J, et al. Large language models for conducting advanced text analytics information systems research. ACM Trans Manag Inf Syst, 2025, 16(1): 1-27.
29. Takagi S, Watari T, Erabi A, et al. Performance of GPT-3. 5 and GPT-4 on the Japanese medical licensing examination: comparison study. JMIR Med Educ, 2023, 9: e48002.
30. 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. Radiol, 2023, 307(4): e230725.
31. Birkhauer J, Gaab J, Kossowsky J, et al. Trust in the health care professional and health outcome: a meta-analysis. PLoS One, 2017, 12(2): e0170988.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesInterpretive radiology reports for lung cancer generated by GPT-4 large language model to enhance doctor–patient communication efficiency

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