"Enhancing Patient Understanding of Radiology Reports Through LLM-Generated Summaries, Clickable Terms, and AI Videos".
Authors
Affiliations (4)
Affiliations (4)
- Emory University, School of Medicine, Atlanta, GA, USA. Electronic address: [email protected].
- Emory University, School of Medicine, Atlanta, GA, USA.
- Augusta University, Medical College of Georgia, Augusta, GA, USA.
- UT Health San Antonio, Long School of Medicine, San Antonio, TX, USA.
Abstract
To evaluate how a custom web application integrating clinician-edited large language model (LLM)-generated summaries, clickable definitions, and artificial intelligence (AI)-generated videos affects radiology report comprehension, feature preferences, and overall sentiment toward AI-assisted report summaries. This prospective study recruited participants between May and July 2025 at a hospital-based outpatient imaging floor before their scheduled examinations at a tertiary university hospital. Following exam completion and report publication, patient-friendly AI summaries were generated and reviewed by a radiologist for accuracy. Participants were then shown a web application containing their own de-identified, AI-augmented reports featuring clinician-edited LLM-generated summaries with clickable terms and AI videos. Participants were surveyed on comprehension, feature usefulness, and attitudes toward LLM summaries. Participants (n=101, 40 male/61 female, racially diverse) ranged from 20 to 82 (mean 58±15) years old. Overall comprehension improved significantly (median pre: 4.00, post: 5.00, p<0.001), with 47.52% (n=48) identifying LLM-summaries as most helpful. However, LLM-summaries required manual clinician edits (average per summary: 24.75 words removed; 0.13 words added, lexical similarity = 84.63%; semantic similarity = 98.25%). When asked if they were comfortable with LLM-summaries without clinician edits, most participants reported being only Somewhat comfortable (27.72%) or Very uncomfortable (25.74%). This prospective study demonstrates that interactive, LLM-driven applications can significantly improve self-reported patient comprehension of complex radiology reports, emphasizing their potential to enhance patient-centered communication. However, patients had reservations about clinician-edited LLM-generated summaries, indicating that successful integration is contingent on professional oversight - an added workload that may limit scalable real-world implementation.