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Related Experiment Video

Updated: Jan 11, 2026

Author Spotlight: Demonstrating Systematic Endobronchial Ultrasound to New Endoscopists

Author Spotlight: Demonstrating Systematic Endobronchial Ultrasound to New Endoscopists

Published on: August 11, 2023

Improving Informed Consent Models for Endobronchial Ultrasound With Artificial Intelligence.

Diana Moreira-Sousa¹, Ana M Oliveira², Sara Ferreira³

¹Department of Pulmonology, Unidade Local de Saúde da Cova da Beira, Alameda Pêro da Covilhã, Covilhã.

Journal of Bronchology & Interventional Pulmonology

|November 11, 2025

Summary

Artificial intelligence (AI) enhances informed consent (IC) for procedures like endobronchial ultrasound (EBUS). AI-generated text and video consents show promise for improving patient understanding and satisfaction, though human oversight is crucial.

Keywords:

artificial intelligence consent forms endoscopic ultrasound-guided fine needle aspiration informed consent natural language processing

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Patient Communication

Background:

Informed consent (IC) is crucial for patient understanding of medical procedures, including endobronchial ultrasound (EBUS).
Artificial intelligence (AI) offers potential solutions to enhance the traditional IC process.
This study investigates AI's role in improving IC documents and evaluating AI-generated video consents.

Purpose of the Study:

To assess the efficacy of AI-generated informed consent (IC) documents compared to traditional ones.
To explore the feasibility and patient acceptance of AI-generated video consents (AIV-IC) as an alternative to verbal IC.
To determine if AI can improve clarity, explanation of benefits, and discussion of complications in IC.

Main Methods:

Development of an AI-generated IC (AI-IC) using a generative AI model.
Phase I: Participants compared AI-IC and traditional IC (H-IC) texts via Likert scale questionnaires.
Phase II: Patients evaluated AI-IC (text) and AIV-IC (video) formats through questionnaires.

Main Results:

AI-IC texts scored higher in language clarity, benefits explanation, and addressing complications than H-IC.
A significant majority preferred AI-IC for its mention of alternative procedures.
Patients reported high satisfaction with both AI-IC and AIV-IC, with AIV-IC being accepted as a replacement for verbal IC.

Conclusions:

AI-generated materials enhance the accessibility of IC for EBUS procedures.
AI-assisted and video-based consent tools show potential for clinical integration.
Human supervision remains essential for AI-driven consent processes.