# Large language models for simplifying radiology reports: a systematic review and meta-analysis of patient, public, and clinician evaluations

## TL;DR

This study reviews how large language models can simplify radiology reports to make them easier for patients to understand, while maintaining accuracy according to clinicians.

## Contribution

The study provides a systematic review and meta-analysis of LLM-based simplification of radiology reports, evaluating patient and clinician perceptions.

## Key findings

- LLM-rewritten reports were rated significantly more understandable by patients than original reports.
- Clinicians rated LLM-simplified reports as highly accurate and complete.
- Readability scores improved across different imaging modalities in LLM-rewritten reports.

## Abstract

Radiology reports are typically written in language that is difficult for patients to understand. Large language models (LLMs) excel at simplifying text. We aimed to evaluate the ability of LLMs to improve the understanding of radiology reports.

In this systematic review and meta-analysis, we searched CENTRAL, MEDLINE, and Embase from inception to Nov 11, 2025, without restrictions on language. Full-text articles and preprints were considered for inclusion. Eligible studies applied LLMs to simplify radiology reports and had these reports assessed by members of the public or medical professionals. We excluded studies that focused solely on dialogues with interactive chatbots, preimaging leaflets, educational materials, appointment letters, or summarising findings without simplifying them for patients. Search results were screened independently by two authors and full-text review and data extraction were done by three authors; disagreements were resolved by consensus. The main outcomes were patient, public, and clinician evaluations (Likert scores) and text readability metrics. We assessed study quality with the MAIC-10 tool. This study was registered with PROSPERO (CRD420251027489).

We identified 2385 records, of which 38 studies were eligible. These 38 studies generated 12 922 simplified reports, assessed by 508 evaluators (387 lay people and 121 clinicians). 35 (92%) of 38 studies used OpenAI GPT models and 29 (76%) produced simplified reports in English. Patients perceived LLM-rewritten reports as significantly more understandable than radiologist reports (mean Likert score 4·04 [SD 1·20] for simplified reports vs 2·16 [SD 0·94] for original reports; mean difference 2·00 [95% CI 1·54–2·46]). Clinicians rated LLM-rewritten reports highly for accuracy (mean 4·45 [95% CI 4·27–4·63]; 27 studies) and completeness (mean 4·53 [95% CI 4·30–4·76]; 14 studies). Readability was improved across imaging modalities, with lower Flesch–Kincaid Grade Level for LLM-rewritten reports, including a mean difference of −6·20 (95% CI −6·91 to −5·48) for CT, −5·07 (−5·99 to −4·15) for x-ray, and −5·0 (−6·0 to −4·0) for MRI. The error rate in LLM-rewritten reports was 7·2% (95% CI 5·1%–10·0%; 13 studies) and 0·9% (95% CI 0·6–1·5%; 2 studies) for clinically significant errors.

LLM-simplified radiology reports improved patient-perceived understanding and readability and were rated by clinicians as largely accurate and complete, although a small proportion contained clinically significant errors. LLM-based simplification shows promise for making radiology communication more patient-centred, but further evaluation of its effect on patient outcomes and clinical workflows is required.

National Institute for Health and Care Research Sheffield Biomedical Research Centre.