Large language model–based simplification of breast imaging reports: a prospective multicentre study with 2965 patient-based evaluations
OBJECTIVE: To compare patient perceptions of radiologist-written and artificial intelligence (AI)-generated simplifications of breast imaging reports in terms of simplicity, comprehensibility, empathy, and overall preference. MATERIALS AND METHODS: In this prospective multicentre survey-based study, 10 anonymized breast imaging reports (5 ultrasound, 5 mammography; one BI-RADS category 1-5 per modality) were simplified by ChatGPT-4 within a 50-word limit. After assessment by a panel of 20 physicians, paired radiologist-written and AI-generated reports were evaluated by 314 patients (2965 evaluations) who rated simplicity, comprehensibility, and empathy on a 5-point Likert scale and indicated their preferred report. The origin of each report was concealed, and each pair was presented in randomized order. Univariable and multivariable mixed-effects logistic regression analyses were performed to identify factors associated with preferring AI-generated reports. RESULTS: AI-generated reports were preferred in 63.3% (1876/2965) of evaluations. High simplicity and comprehensibility scores (4-5) were more frequently assigned to AI reports than to radiologist-written reports (70.0% vs 43.4% and 68.1% vs 40.4%, respectively). In multivariable analysis, higher comprehensibility was the strongest predictor in both directions: higher comprehensibility of AI-generated reports increased the odds of preferring AI (score 4: adjusted OR 1.87, 95% CI 1.08-3.23, p = 0.025), while higher comprehensibility of radiologist-written reports reduced them (score 4: adjusted OR 0.45, 95% CI 0.29-0.69, p < 0.001). Preference for AI-generated reports showed a non-significant trend toward increase with educational level. CONCLUSION: AI-generated breast imaging reports were preferred by most patients and perceived as clearer, supporting a complementary and supervised use of AI-generated summaries. KEY POINTS: Question Patients increasingly access their own radiology reports, yet standard radiologist-written reports are often difficult to understand, highlighting the need for patient-friendly alternatives. Findings In this study, AI-generated breast imaging reports were preferred by patients in 63.3% of evaluations, with comprehensibility emerging as the strongest predictor of preference. CRITICAL RELEVANCE STATEMENT: AI-generated breast imaging reports were preferred by most patients and received higher ratings for simplicity and comprehensibility than radiologist-written reports, supporting a supervised and complementary use of AI-generated summaries in clinical breast imaging.
Authors
- Sonia Santicchia
- Giulia Querques (ORCID: https://orcid.org/0000-0001-5189-9915)
- Maria Iodice
- Alessandro Liguori (ORCID: https://orcid.org/0000-0002-1227-1671)
- Mario R. Pannarale
- Filippo Pesapane (ORCID: https://orcid.org/0000-0002-0374-5054)
- Andrea Cozzi (ORCID: https://orcid.org/0000-0003-4922-7065)
- Veronica Magni (ORCID: https://orcid.org/0000-0002-4240-0059)
- Gianpaolo Carrafiello (ORCID: https://orcid.org/0000-0002-8002-4277)
- Marta Montesano
- Maurizio Cè (ORCID: https://orcid.org/0000-0002-8906-5665)
- Sveva Mortellaro (ORCID: https://orcid.org/0009-0004-3735-5494)
- Luca Nicosia (ORCID: https://orcid.org/0000-0003-3979-8075)
- Matilde Pavan (ORCID: https://orcid.org/0009-0004-4374-7806)
- Serena Carriero (ORCID: https://orcid.org/0000-0003-2583-4215)
- Giulia Quercioli
- Maria Cosentino
Institutions
- University of Milan (IT)
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT)
- Ente Ospedaliero Cantonale (CH)
- Ospedale A. Perrino (IT)
- Ospedale di Stato della Repubblica di San Marino (SM)
- European Institute of Oncology (IT)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2026-11-02
- DOI
- https://doi.org/10.1186/s13244-026-02423-0
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00