Improving patient communication by simplifying AI-generated dental radiology reports with ChatGPT : comparative study

dc.contributor.authorStephan, Daniel
dc.contributor.authorBertsch, Annika
dc.contributor.authorSchumacher, Sophia
dc.contributor.authorPuladi, Behrus
dc.contributor.authorBurwinkel, Matthias
dc.contributor.authorAl-Nawas, Bilal
dc.contributor.authorKämmerer, Peer W.
dc.contributor.authorThiem, Daniel G. E.
dc.date.accessioned2026-07-17T07:58:44Z
dc.date.issued2025
dc.description.abstractBackground: Medical reports, particularly radiology findings, are often written for professional communication, making them difficult for patients to understand. This communication barrier can reduce patient engagement and lead to misinterpretation. Artificial intelligence (AI), especially large language models such as ChatGPT, offers new opportunities for simplifying medical documentation to improve patient comprehension. Objective: We aimed to evaluate whether AI-generated radiology reports simplified by ChatGPT improve patient understanding, readability, and communication quality compared to original AI-generated reports. Methods: In total, 3 versions of radiology reports were created using ChatGPT: an original AI-generated version (text 1), a patient-friendly, simplified version (text 2), and a further simplified and accessibility-optimized version (text 3). A total of 300 patients (n=100, 33.3% per group), excluding patients with medical education, were randomly assigned to review one text version and complete a standardized questionnaire. Readability was assessed using the Flesch Reading Ease (FRE) score and LIX indices. Results: Both simplified texts showed significantly higher readability scores (text 1: FRE score=51.1; text 2: FRE score=55.0; and text 3: FRE score=56.4; P<.001) and lower LIX scores, indicating enhanced clarity. Text 3 had the shortest sentences, had the fewest long words, and scored best on all patient-rated dimensions. Questionnaire results revealed significantly higher ratings for texts 2 and 3 across clarity (P<.001), tone (P<.001), structure, and patient engagement. For example, patients rated the ability to understand findings without help highest for text 3 (mean 1.5, SD 0.7) and lowest for text 1 (mean 3.1, SD 1.4). Both simplified texts significantly improved patients’ ability to prepare for clinical conversations and promoted shared decision-making. Conclusions: AI-generated simplification of radiology reports significantly enhances patient comprehension and engagement. These findings highlight the potential of ChatGPT as a tool to improve patient-centered communication. While promising, future research should focus on ensuring clinical accuracy and exploring applications across diverse patient populations to support equitable and effective integration of AI in health care communication.en
dc.identifier.doihttps://doi.org/10.25358/openscience-15713
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/15734
dc.language.isoeng
dc.rightsCC-BY-4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc610 Medizinde
dc.subject.ddc610 Medical sciencesen
dc.titleImproving patient communication by simplifying AI-generated dental radiology reports with ChatGPT : comparative studyen
dc.typeZeitschriftenaufsatz
jgu.apc.netprice2939,96
jgu.apc.price3155,92
jgu.apc.taxrate7
jgu.dfg.year2025
jgu.identifier.uuid740ed2de-dee4-4284-80be-5f23b3f51759
jgu.journal.titleJournal of medical internet research
jgu.journal.volume27
jgu.nationalcurrency.usd3350,00
jgu.organisation.departmentFB 04 Medizin
jgu.organisation.nameJohannes Gutenberg-Universität Mainz
jgu.organisation.number2700
jgu.organisation.placeMainz
jgu.organisation.rorhttps://ror.org/023b0x485
jgu.pages.alternativee73337
jgu.publisher.doi10.2196/73337
jgu.publisher.eissn1438-8871
jgu.publisher.nameHealthcare World
jgu.publisher.placeRichmond, Va.
jgu.publisher.year2025
jgu.rights.accessrightsopenAccess
jgu.subject.ddccode610
jgu.subject.dfgLebenswissenschaften
jgu.type.dinitypeArticleen_GB
jgu.type.resourceText
jgu.type.versionPublished version

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