ArticleJournal of medical Internet research2025
Enhancing Physician-Patient Communication in Oncology Using GPT-4 Through Simplified Radiology Reports: Multicenter Quantitative Study.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review.Journal of medical Internet research · 2026Article
- Ethical Governance of Large Language Models in Health Care: Trust, Responsibility, and Equity in Routine Use.Journal of medical Internet research · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
- Potential of large language models for rapid clinical information support: evidence from acute kidney injury knowledge testing.Scientific reports · 2026Article
- Self-reported comprehension of large language model-generated summaries of lung cancer screening reports: a vignette survey.Radiology advances · 2026Article
- Self-Reflective Chest X-Ray Report Generation with Clinical-Aware Detection and Multilevel Readability.Journal of medical systems · 2025Article
- Factors Influencing Adoption of Large Language Models in Health Care: Multicenter Cross-Sectional Mixed Methods Observational Study.Journal of medical Internet research · 2025Observational
- Using ChatGPT-4 for Lay Summarization in Prostate Cancer Research to Advance Patient-Centered Communication: Large-Scale Generative AI Performance Evaluation.Journal of medical Internet research · 2025Article
- Harnessing GPT-4 for automated error detection in pathology reports: Implications for oncology diagnostics.Digital healthArticle
- Article
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Authors and funding
17 authors.
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Abstract
backgroundEffective 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.
objectiveWe aimed to assess the feasibility and effectiveness of using GPT-4 to simplify oncological radiology reports to improve physician-patient communication.
methodsIn 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.
resultsTransforming 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).
conclusionsThis 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.
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