Comparison of artificial intelligence models and physicians in patient education for varicocele embolization: a double-blind randomized controlled trial
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Summary
This summary is machine-generated.Artificial intelligence (AI) models, like Gemini, outperformed a physician in educating patients about varicocele embolization, showing superior academic accuracy and empathy. This suggests AI can enhance patient education in interventional radiology.
Area Of Science
- Medical Imaging and Interventional Radiology
- Artificial Intelligence in Healthcare
- Patient Education and Communication
Background
- Few studies compare large language models (LLMs) with clinicians in patient information delivery.
- Evaluating AI performance is crucial for developing hybrid informational systems.
- Varicocele embolization patient education needs rigorous assessment.
Purpose Of The Study
- To compare AI models and clinical specialists in informing patients about varicocele embolization.
- To establish an evidence base for AI-assisted patient education.
- To assess AI's academic accuracy and empathetic communication.
Main Methods
- Prospective, double-blind, randomized controlled trial.
- 25 frequently asked questions on varicocele embolization were answered by 3 AI models and 1 interventional radiologist.
- Responses were evaluated by 2 independent radiologists on academic accuracy and empathy using a 5-point Likert scale.
Main Results
- Gemini AI achieved the highest scores for academic accuracy (4.09) and empathy (3.54).
- AI models, including Copilot and ChatGPT, generally scored higher than the comparator physician in accuracy and empathy.
- Statistically significant differences were found across groups for both academic accuracy and empathy.
Conclusions
- AI models, particularly Gemini, demonstrate superior performance in academic accuracy and empathetic communication for varicocele embolization patient education compared to a physician.
- AI holds significant potential to augment patient education systems in interventional radiology.
- Findings support the development of hybrid AI-clinician patient education models.
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