Journal of bronchology & interventional pulmonology · 2025
Improving Informed Consent Models for Endobronchial Ultrasound With Artificial Intelligence.
СтатьяНазвание и аннотация: Перевод в очереди
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Moreira-Sousa Diana, Oliveira Ana M, Ferreira Sara, Furtado Filipa, Aguiar Ana, Terras André, Sousa Catarina, Freitas Cláudia, Machado Francisco, Fernandes Gabriela, Jacob Maria, Magalhães Adriana, Novais E Bastos Hélder
Аннотация
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.
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.