ArticleInsights into imaging2025
Comparative Evaluation of Large Language Models in Explaining Radiology Reports: Expert Assessment of Readability, Understandability, and Communication Features.
Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large language models for simplifying radiology reports: a systematic review and meta-analysis of patient, public, and clinician evaluations.The Lancet. Digital health · 2026Pooled it
- From zero-shot to fine-tuning: optimize large language models for error detection of ultrasound reports.Insights into imaging · 2026Article
- Assessment of AI-Generated Patient Education Materials for Bladder Training and Pelvic Floor Muscle Therapy: Comparison with an International Society Leaflet.International urogynecology journal · 2026Article
- Perceived communication quality of a standardized neuroradiology report following AI-based simplification: a randomized, blinded study.Insights into imaging · 2026Article
- Large Language Model Translation of BI-RADS Breast Imaging Reports into Arabic: A Blinded Expert Evaluation of Diagnostic Communication Safety.Diagnostics (Basel, Switzerland) · 2026Article
- Large Language Models in Radiologist-Patient Communication: A Narrative Review for Clinical Practice.Cureus · 2026Review
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Authors and funding
2 authors.
Funding
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Abstract
objectivesTo compare understandability, readability, and communication characteristics of radiology report explanations generated by three freely accessible large language models-ChatGPT, Gemini, and Copilot-based on a standardized prompt, as assessed by expert reviewers. MATERIALS AND
methodsIn this retrospective single-center study, 100 anonymized radiology reports were randomly selected from five subspecialties. Each report was submitted to ChatGPT (GPT-3.5), Gemini, and Copilot between May 23 and May 30, 2025, using the prompt, "Can you explain my radiology report?". Responses were evaluated for medical correctness on a 3-point scale (0-2), understandability using the patient education materials assessment tool for understandability (PEMAT-U), and readability using Flesch Reading Ease (FRE), Automated Readability Index (ARI), and Gunning Fog Index (GFI). Communicative features-including uncertainty language, patient guidance, and clinical suggestions-were also assessed. Anxiety-inducing potential was rated on a 3-point Likert scale.
resultsAll models demonstrated high medical correctness (mean: 1.97 ± 0.17/2). ChatGPT produced the most readable (FRE: 60.33 ± 3.65; ARI: 9.66 ± 1.01; GFI: 9.1 ± 1.04) and understandable (PEMAT-U: 89.58 ± 3.90%) responses (p < 0.01). Copilot included the most uncertainty language (1.62 ± 0.62) and clinical suggestions (1.69 ± 0.60), while Gemini provided the strongest patient guidance (1.62 ± 0.58) (all p < 0.01). Only Copilot showed subspecialty-related variation in readability (GFI; p = 0.048). Anxiety potential was low across all models (mean: 0.07 ± 0.33).
conclusionChatGPT offered superior readability and understandability. Copilot delivered more clinical detail with cautious language, while Gemini emphasized patient-centered guidance. These differences support context-specific use of language models in radiology communication. CRITICAL RELEVANCE STATEMENT: This study shows that freely accessible large language models produce radiology report explanations with varying levels of readability, understandability, and communication quality. Expert-based findings may help inform future strategies to optimize patient-facing applications of AI in radiological communication. KEY POINTS: This study compared how freely available AI chatbots respond to patient queries about radiology reports. Significant differences were found in understandability, readability, patient guidance, and use of uncertainty or clinical suggestions. Findings support context-specific use of AI tools to improve radiology communication and patient understanding.
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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.