Development and Clinical Evaluation of a Large Language Model-Based System for Generating Patient-Friendly Echocardiography Reports: Two-Stage Retrospective Validation and Prospective Survey Study.

Background: Standard echocardiography reports use complex terminology, limiting patient comprehension and exacerbating preconsultation anxiety. Large language models (LLMs) can transform technical data into patient-friendly narratives by incorporating longitudinal comparisons with prior examinations. Objective: This study aims to develop an LLM-based patient-friendly echocardiography reporting system and evaluate its professional safety, patient comprehension, and impact on short-term anxiety. Methods: This study consisted of 2 stages. In the retrospective development stage, 60 patients were included. Clinical diagnosis, hospitalization records, and serial echocardiographic data were integrated as model inputs using DeepSeek-V3.2. Generated reports followed a standardized 4-module structure. Report quality was independently evaluated by 2 clinicians and an external LLM (Kimi 2.5, Moonshot AI) across 4 domains: data accuracy, information completeness, appropriateness of interpretation, and reasonableness of recommendations. In the prospective clinical evaluation stage, 100 patients undergoing echocardiography and 85 family members were enrolled. Participants received both conventional and LLM-generated patient-friendly reports. A 5-point Likert scale assessed helpfulness in understanding results, effectiveness in addressing concerns, helpfulness in improving disease-related knowledge, and anxiety relief. Anxiety was measured using the STAI-6 (6-item short-form State-Trait Anxiety Inventory) at 3 time points: after echocardiography, after conventional report release, and after reading the patient-friendly report. Results: All 60 reports in the retrospective stage were successfully generated. Professional evaluation showed high overall quality scores from both clinicians and the external LLM, with no significant difference between evaluators (mean total scores 18.15, SD 1.36 vs 18.28, SD 1.26; P=.55). One hallucination event was identified. In the prospective stage, all 100 patients received patient-friendly reports. Both patients and family members rated the reports highly, with no significant between-group difference in total scores (17.61, SD 1.60 vs 17.62, SD 1.03; P=.95). Subgroup analyses showed greater perceived benefit among older patients and outpatients (both P<.001); these subgroup findings should be considered exploratory given the lack of adjustment for multiple comparisons. Patients with chronic heart failure, reduced left ventricular ejection fraction (≤40%), and left ventricular enlargement (>55 mm) reported higher scores for addressing concerns (all P<.001). Anxiety scores increased significantly after conventional report release and decreased significantly after reading the patient-friendly report (both P<.001). Older patients (>60 y) and outpatients showed significantly higher anxiety change rates than their counterparts (both P<.001). The reduction in anxiety was positively correlated with subjective anxiety relief ratings (r=0.531; P<.001). Conclusions: The LLM-based patient-friendly echocardiography reporting system with longitudinal comparison demonstrated good feasibility and promising preliminary clinical usefulness. While maintaining high professional quality, it was associated with improved patient understanding of echocardiographic findings and was associated with reduced short-term anxiety, particularly among older adults and outpatients. Causality cannot be inferred from this nonrandomized sequential design, and longer-term outcomes remain to be evaluated.