Datta, R. (2025). When AI Joins the scope: Canadian endoscopists’ perceptions of NodeAI versus conventional methods for identifying lymph node malignancies in EBUS imaging. The Young Researcher, 9(1), 196-213. http://www.theyoungresearcher.com/papers/datta.pdf

Abstract
This study investigates the potential impact of integrating NodeAI, an AI-assisted tool, into endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) procedures for cancer staging and lymph node (LN) biopsy. Using a convergent parallel design mixed-methods approach, nine experienced participants, including thoracic surgeons and pulmonologists in North America, were surveyed and participated in a focus group to assess their perspectives on AI integration into EBUS-TBNA cases. The baseline and endline surveys measured shifts in opinions regarding NodeAI's usability in diagnostic accuracy, procedure time, and ease of use. Qualitative insights were gathered through open-ended questions during the focus group to explore clinicians' views on AI's potential role, while quantitative data was captured using scales/ratings. The study found that, while most participants expressed satisfaction with current EBUS-TBNA practices, concerns around over-reliance on AI, data privacy, and the technology's accuracy surfaced during discussion. However, following exposure to NodeAI, participants' views became more favorable, with an increased likelihood of incorporating AI into clinical practice. Key benefits identified included improved diagnostic speed, reduced false positives/negatives, and potential cost savings. The findings suggest that AI tools like NodeAI could enhance decision-making, reduce procedure time and resources, while also presenting challenges related to workflow integration and overreliance, especially for less experienced individuals.


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