Innovation AI used to help improve lung cancer biopsies July 8, 2026 TORONTO and HAMILTON, Ont. – For patients awaiting lung cancer staging results, delays carry real consequences. Staging, in other words, determining whether cancer has spread beyond the lung to nearby lymph nodes, shapes the entire treatment pathway: surgery, chemotherapy, radiation, or palliative care. The standard procedure for that determination is endobronchial ultrasound-guided transbronchial needle aspiration, or EBUS-TBNA, a minimally invasive technique that uses a flexible scope and ultrasound imaging to sample lymph nodes in the chest without open surgery. It is well-established, widely performed, and highly effective when performed by appropriately trained clinicians – though its diagnostic performance can vary. Published research has associated variability in EBUS-TBNA diagnostic outcomes with operator experience and procedural volume, among other factors. When results are inconclusive, patients return for repeat procedures, extending uncertainty in a disease where the speed and accuracy of cancer staging matters. Addressing that variability is a key research question at the centre of a new McMaster University led clinical trial now underway, with University Health Network’s Toronto General Hospital as a key site. The study aims to evaluate the impact of an AI-assisted intervention developed by Node AI on EBUS-TBNA performance. The case for AI assistance Node AI, a Hamilton-based medical AI company co-founded by thoracic surgeon Dr. Wael Hanna, AI scientist Dr. Anthony Gatti, and CEO Mackensey Bacon, has developed a real-time decision-support platform designed to assist clinicians performing EBUS-TBNA. The platform is designed to integrate into existing EBUS clinical workflows via a cloud-based interface, requiring no additional hardware and with the intention of supporting all major bronchoscope manufacturers, the specialized scopes used to navigate and image the airways during the procedure. During a biopsy, the AI is designed to analyze ultrasound video in real-time, with the