Enhancing Doctor-Patient Communication Using Large Language Models for Pathology Report Interpretation

Abstract Background Large language models (LLMs) are increasingly utilized in healthcare settings. Postoperative pathology reports, which are essential for diagnosing and determining treatment strategies for surgical patients, frequently include complex data that can be challenging for patients to comprehend. This complexity can adversely affect the quality of communication between doctors and patients, potentially impacting patient outcomes. Materials and Methods This study analyzed text pathology reports from four hospital between October and December 2023, focusing on malignant tumors. Using GPT-4, we developed templates for interpretive pathology reports (IPRs) to simplify medical terminology for non-professionals. We randomly selected 70 reports to generate these templates and evaluated the remaining 628 reports for consistency and readability. Results Among 698 pathology reports analyzed, the interpretation through LLMs significantly improved readability and patient understanding. The average communication time between doctors and patients decreased by over 70%, from 35 minutes to 10 minutes (P P < 0.001), with the use of IPRs. indicating an effective translation of complex medical information. Conclusion This research demonstrates the efficacy of LLMs like GPT-4 in enhancing doctor-patient communication by translating pathology reports into more accessible language. By improving comprehension and reducing communication time, AI applications in medical settings can enhance patient outcomes and satisfaction, highlighting the potential of AI to bridge gaps between medical professionals and the public in healthcare environments.