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.
