Evidence map›Paper›PMID 42791420›Full record

ArticleOral and maxillofacial surgery2026

Improving patient communication and understanding of medical reports through text simplification with ChatGPT: a comparative study.

Daniel Stephan, Sophia Schumacher, Bilal Al-Nawas, Peer W Kämmerer, Daniel G E Thiem

Abstract readComparative Study
In one paragraph

Article in Oral and maxillofacial surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Daniel StephanDepartment of Oral and Maxillofacial Surgery, Facial Plastic Surgery, University Medical Centre of the Johannes Gutenberg-University Mainz, Augustusplatz 2, 55131, Mainz, Germany. stephand@uni-mainz.de.
Sophia SchumacherDepartment of Oral and Maxillofacial Surgery, Facial Plastic Surgery, University Medical Centre of the Johannes Gutenberg-University Mainz, Augustusplatz 2, 55131, Mainz, Germany.
Bilal Al-NawasDepartment of Oral and Maxillofacial Surgery, Facial Plastic Surgery, University Medical Centre of the Johannes Gutenberg-University Mainz, Augustusplatz 2, 55131, Mainz, Germany.
Peer W KämmererDepartment of Oral and Maxillofacial Surgery, Facial Plastic Surgery, University Medical Centre of the Johannes Gutenberg-University Mainz, Augustusplatz 2, 55131, Mainz, Germany.
Daniel G E ThiemDepartment of Oral and Maxillofacial Surgery, Facial Plastic Surgery, University Medical Centre of the Johannes Gutenberg-University Mainz, Augustusplatz 2, 55131, Mainz, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeMedical documentation is essential for clinical communication but is often intended for professional audiences, limiting patient understanding. This linguistic complexity can reduce health literacy and hinder shared decision-making. Large language models offer new opportunities to simplify medical texts while maintaining factual accuracy, thereby improving accessibility and comprehension. This study aimed to evaluate whether ChatGPT can simplify medical reports while preserving clinical content and to assess whether these simplifications improve patient comprehension, readability, and perceived communication quality.

methodsFive document types were analysed, including MRI, CT, surgical, pathology reports, and discharge summaries. Each original physician-written document was simplified using a standardized ChatGPT prompt instructing full content preservation and patient-oriented phrasing. Ten simplifications per text were reviewed for completeness. Readability was measured using Flesch Reading Ease (FRE) and LIX indices. A total of 576 participants without medical knowledge evaluated either a simplified or original report version and completed a standardized questionnaire assessing clarity, structure, and applicability, followed by comprehension testing.

resultsSimplified reports achieved significantly higher readability (FRE 48.4 ± 5.0 vs. 22.9 ± 5.2; p < 0.0001; LIX 48.3 ± 3.2 vs. 59.2 ± 2.5; p = 0.004). Across all document types, patients rated simplified texts significantly higher in clarity, structure, and usefulness (p < 0.001), with significantly improved comprehension accuracy (e.g., MRI 80.3% vs. 53.7%; p < 0.001). No loss of medical information was observed.

conclusionsChatGPT appears to be capable of simplifying complex medical documents while preserving clinically relevant information, leading to improvements in both perceived readability and objective understanding under the conditions of this study. AI-driven text simplification thus represents a promising tool to enhance patient communication and health literacy.

Indexed as

CommunicationComprehensionDocumentationHealth LiteracyPhysician-Patient RelationsAdultFemaleGenerative Artificial IntelligenceHumansLarge Language ModelsMaleMiddle AgedPlain Language SummariesSurveys and QuestionnairesClinical communicationHealth literacyMedical accessibilityNatural language processingPatient-centered communicationPatient comprehension

Identifiers

PMID42791420
PMCPMC13614978

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.