Improving Informed Consent Models for Endobronchial Ultrasound with Artificial Intelligence

Abstract

Background: Informed consent (IC) ensures patient understanding on proposed medical procedures, including endobronchial ultrasound (EBUS). Artificial intelligence (AI) presents as a potential tool to improve this process. Research Question: This study explores the potential of AI to improve traditional IC documents, and if AI-generated video consents are a feasible alternative. Methods: An AI-generated IC (AI-IC) was created using a generative AI model. In Phase I, participants evaluated both AI-IC and traditional IC (H-IC) unidentified texts through a 5-point Likert scale questionnaire and selected their preferred. In Phase II, patients answered a questionnaire evaluating the AI-generated IC in text (AI-IC) or video (AIV-IC) format. Results: In Phase I, (n=75, 44% healthcare professionals), AI-IC received higher scores for language clarity (p=0.013), benefits explanation (p<0.001), and addressing complications (p<0.001), but had lower scores for detailing the procedure (p<0.001). Most participants (86.7%) preferred the AI-IC for mentioning alternative procedures. In Phase II, patients expressed high satisfaction with both the AI-IC (n=8) and AIV-IC (n=12). AIV-IC was globally accepted for replacing verbal IC. Conclusion: AI-generated materials improve accessibility, and comprehension in the IC for EBUS. While human supervision remains essential, future studies could strengthen the integration of AI-assisted and video-based consent tools in clinical practice.