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. 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% health care 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 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.

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