J Med Internet Res. 2025 Apr 17;27:e63786. doi: 10.2196/63786.
ABSTRACT
BACKGROUND: Effective physician-patient communication is essential in clinical practice, especially in oncology, where radiology reports play a crucial role. These reports are often filled with technical jargon, making them challenging for patients to understand and affecting their engagement and decision-making. Large language models, such as GPT-4, offer a novel approach to simplifying these reports and potentially enhancing communication and patient outcomes.
OBJECTIVE: We aimed to assess the feasibility and effectiveness of using GPT-4 to simplify oncological radiology reports to improve physician-patient communication.
METHODS: In a retrospective study approved by the ethics review committees of multiple hospitals, 698 radiology reports for malignant tumors produced between October 2023 and December 2023 were analyzed. In total, 70 (10%) reports were selected to develop templates and scoring scales for GPT-4 to create simplified interpretative radiology reports (IRRs). Radiologists checked the consistency between the original radiology reports and the IRRs, while volunteer family members of patients, all of whom had at least a junior high school education and no medical background, assessed readability. Doctors evaluated communication efficiency through simulated consultations.
RESULTS: Transforming original radiology reports into IRRs resulted in clearer reports, with word count increasing from 818.74 to 1025.82 (P<.001), volunteers’ reading time decreasing from 674.86 seconds to 589.92 seconds (P<.001), and reading rate increasing from 72.15 words per minute to 104.70 words per minute (P<.001). Physician-patient communication time significantly decreased, from 1116.11 seconds to 745.30 seconds (P<.001), and patient comprehension scores improved from 5.51 to 7.83 (P<.001).
CONCLUSIONS: This study demonstrates the significant potential of large language models, specifically GPT-4, to facilitate medical communication by simplifying oncological radiology reports. Simplified reports enhance patient understanding and the efficiency of doctor-patient interactions, suggesting a valuable application of artificial intelligence in clinical practice to improve patient outcomes and health care communication.
PMID:40245397 | DOI:10.2196/63786
