Journal of the American College of Radiology : JACR · 2026
Enhancing Patient Understanding of Radiology Reports Through Large Language Model-Generated Summaries, Clickable Terms, and Artificial Intelligence Videos.
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Li Hanzhou, Moon John T, Balthazar Patricia, Nguyen Oanh, Patel Emily, Kasetty Ekaa, Veluri Shriya, Bercu Zachary, Newsome Janice, Krupinski Elizabeth, Gichoya Judy, Trivedi Hari
Аннотация
PURPOSE: 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.
MATERIALS AND METHODS: 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. After examination 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.
RESULTS: Participants (n = 101, 40 male and 61 female, racially diverse subjects) ranged from 20 to 82 (mean 58 ± 15) years old. Overall comprehension improved significantly (median before using the web application: 4.00, median after: 5.00, P < .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%).
CONCLUSION: 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.
MATERIALS AND METHODS: 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. After examination 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.
RESULTS: Participants (n = 101, 40 male and 61 female, racially diverse subjects) ranged from 20 to 82 (mean 58 ± 15) years old. Overall comprehension improved significantly (median before using the web application: 4.00, median after: 5.00, P < .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%).
CONCLUSION: 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.