AI-generated patient friendly MRI fistula summaries: a pilot randomised study
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Perianal fistulising Crohn’s disease (pfCD) affects 1 in 5 Crohn’s patients and requires frequent MRI monitoring. Standard radiology reports are written for clinicians using technical language often inaccessible to patients, which can cause anxiety and hinder engagement. This study evaluates the feasibility and safety of AI-generated patient-friendly MRI fistula summaries to improve patient understanding and shared decision-making. MRI fistula reports spanning healed to complex disease were identified and used to generate AI patient-friendly summaries via ChatGPT-4. Six de-identified MRI reports and corresponding AI summaries were assessed by clinicians for hallucinations and readability (Flesch-Kincaid score). Sixteen patients with perianal fistulas were randomized to review either AI summaries or original reports and rated them on readability, comprehensibility, utility, quality, follow-up questions, and trustworthiness using Likert scales. Patients rated AI summaries significantly higher in readability (median 5 vs. 2, p = 0.011), comprehensibility (5 vs. 2, p = 0.007), utility (5 vs. 3, p = 0.014), and overall quality (4.5 vs. 4, p = 0.013), with fewer follow-up questions (3 vs. 4, p = 0.018). Clinicians found AI summaries more readable (mean Flesch-Kincaid 54.6 vs. 32.2, p = 0.005) and free of hallucinations. No clinically significant inaccuracies were identified. AI-generated patient-friendly MRI summaries have potential to enhance patient communication and clinical workflow in pfCD. Larger studies are needed to validate clinical utility, hallucination rates, and acceptability.
Date Issued
2025-09-01
Date Acceptance
2025-09-02
Citation
Journal of Imaging, 2025, 11 (9)
ISSN
2313-433X
Publisher
MDPI AG
Journal / Book Title
Journal of Imaging
Volume
11
Issue
9
Copyright Statement
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
302
Date Publish Online
2025-09-04