Title : AI driven patient navigation enables a 12 day pulmonary nodule detection to diagnosis pathway for stage IB lung cancer in El Salvador
Abstract:
Introduction: Pulmonary nodules detected on imaging frequently fail to translate into timely diagnoses due to fragmented and gaps in follow-ups. Even technically successful AI detection of lung nodules on imaging may be clinically redundant in the absence of structured and gap free patient navigation. We report a case from El Salvador where integrated AI chest X-ray (CXR) analysis and AI enabled patient navigation achieved a Stage IB lung cancer diagnosis within 12 days of nodule detection.
Case Presentation: A 70 year non-smoker female, was referred from a Primary Care Unit (PCU) to hospital for CT thorax on December 22, 2025. The CT report identified a pulmonary nodule; however, no follow-up pathway was triggered, and no subsequent step was arranged. The patient returned to the PCU few days later to receive the results of CT scan. The review found abnormality in CT and patient was referred to hospital for next steps. On January 28, 2026, a CXR was performed at the hospital and analysed by AI (qXR), which detected the pulmonary nodule and patient was automatically added into the qTrack (AI) patient navigation workflow. A navigator reviewed the full imaging (CT) history of the patient and in a multidisciplinary team (MDT) biopsy was recommended as a next step. Biopsy was performed on February 2, 2026, just five days after qXR detection of nodule and confirmed Lung Cancer (Adenocarcinoma) Stage IB (T2a NO MO) in Right Middle Lobe on February 9, 2026.
Discussion: This case illustrates the gap between nodule detection and clinical action. The CT detection of pulmonary nodule on December 22 highlighted the gap in follow ups as there was no structured pathway suggested to the patient for nodule follow up. When the same nodule was flagged by qXR six weeks later, qTrack immediately embedded it within an accountable workflow. MDT escalation was done, and biopsy scheduling happened in five days. The CT to CXR imaging sequence reflects a clinical regression highlighting care fragmentation, and gaps in nodule management. This case demonstrates qTrack`s value of plugging the follow up gaps for lung nodules and ensuring every nodule gets appropriate care.
Conclusion: This real-world case from El Salvador demonstrates that AI-powered structured patient navigation can close the follow-up gap and achieve early-stage lung cancer diagnosis in resource-constrained settings. A 40-day trajectory was compressed to 12 days with a Stage IB outcome, a surgically curable stage with >70% five-year survival. Nodule detection without effective navigation is insufficient, thus demonstrating the value of AI (qTrack) enabled navigation which ensures early-stage lung cancer diagnosis.
