Cross-platform evaluation of LLM-generated educational texts on cardiac myxoma: quality, readability, and actionability using network analysis and latent profile analysis - Report - MDSpire

Cross-platform evaluation of LLM-generated educational texts on cardiac myxoma: quality, readability, and actionability using network analysis and latent profile analysis

  • By

  • Bo Deng

  • Zhiqiang Wang

  • Tong Cheng

  • Zhiwen Zhang

  • Muwei Li

  • June 10, 2026

  • 0 min

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Clinical Report: Assessment of LLM-Generated Educational Materials on Cardiac Myxoma

Overview

This study evaluates the quality, readability, and actionability of educational materials generated by large language models (LLMs) for cardiac myxoma. Findings indicate significant variability in text quality and low clinical actionability, highlighting the limitations of current LLM applications in patient education.

Background

Cardiac myxoma is the most common primary cardiac tumor, often leading to serious complications if not managed properly. Effective patient education is crucial for early symptom recognition and adherence to follow-up care. The integration of LLMs in generating educational materials presents a novel approach, yet their effectiveness in conveying complex medical information remains under scrutiny.

Data Highlights

No numerical data available in the source material.

Key Findings

['Significant heterogeneity in information quality and readability across LLM-generated texts.', 'Clinical actionability of the generated materials was generally low.', 'Longer texts correlated positively with higher informational quality scores.', 'Reading difficulty negatively impacted the actionability of the materials.', 'Three distinct text phenotypes were identified: moderate-quality/low-readability, high-quality/high-actionability, and low-quality/easy-to-read.', 'Only a small proportion of outputs were deemed ideally suited for patient education.']

Clinical Implications

Healthcare professionals should be cautious when utilizing LLM-generated materials for patient education, as these texts may not adequately support patient understanding or action. Continuous evaluation and refinement of these educational tools are necessary to enhance their effectiveness in clinical settings.

Conclusion

The current use of LLMs in generating patient education materials for cardiac myxoma reveals significant limitations in readability and actionability. Further research is needed to improve the quality of these resources for better patient outcomes.

Related Resources & Content

  1. npj Digital Medicine, 2025 -- Assessing the Role of Large Language Models in Preoperative and Discharge Education: A Systematic Review Utilizing an Evaluation Framework
  2. European Radiology, 2025 -- Evaluating the Efficacy of Seven Optimized Open-Source Large Language Models for Summarizing and Predicting Outcome-Related Data from Mechanical Thrombectomy Reports in Acute Ischemic Stroke Patients
  3. Obesity Surgery, 2025 -- Evaluating the Efficacy of Large Language Models in Metabolic Bariatric Surgery: A Comparative Analysis
  4. Frontiers in Digital Health, 2026 -- Performance of large language models in delivering accurate and comprehensible patient information on heart failure and cardiomyopathy
  5. Cardiac myxoma: a comprehensive review | Journal of Cardiothoracic Surgery | Full Text
  6. Myxoma: Symptoms, Causes & Treatment
  7. Cardiac myxoma: a comprehensive review | Journal of Cardiothoracic Surgery | Full Text
  8. Myxoma: Symptoms, Causes & Treatment

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