Artificial Intelligence Guided Navigation of High-Risk Incidental Findings on Imaging Studies Leads to Cancer Diagnoses

Why clinicians should know about it

• Picked for Health Informatics (top studies of the week, 8 November 2026): Deployed NLP navigation links imaging findings to cancer care

Abstract

INTRODUCTION: Artificial intelligence (AI) is a burgeoning tool in the field of surgery. AI technology is vastly unexplored with few applications currently in use. High-risk incidental findings in radiologic studies are recognized as a critical source of missed or delayed cancer diagnoses due to gaps in communication, manual tracking burdens, and limited resources. AI has the potential to fundamentally improve recognition of high-risk incidental findings on imaging studies and aid with navigation to timely oncologic care. METHODS: We performed a cohort study that used a commercial end-to-end oncology focused natural language processing solution that used machine learning to identify and track high-risk incidental findings in real-time. The software reviewed radiology reports for imaging studies performed at emergency and outpatient imaging facilities between January 6, 2024 and June 30, 2025. The software model was provided with keywords and parameters to identify high-risk lung, liver, and pancreas lesions for malignancy. The software identified hits among the reports and then a high-risk incidental findings team manually reviewed the radiology reports for validation. Patients with new high-risk findings without established oncologic care were navigated. RESULTS: Over 13 mo, 961,298 radiology reports were reviewed by the software and 48,605 (5%) reports had high-risk lesions identified. The high-risk incidental findings team reviewed all 48,605 imaging reports and confirmed 43,708 clinically significant high-risk findings. AI software demonstrated a rate of 4.5% high-risk incidental findings among all scans reviewed, and a 0.5% false positive rate. Among the confirmed high-risk incidental findings group, 4833 patients did not have an established specialty provider. In addition, 445 of these patients were successfully navigated to multidisciplinary care. After additional work up, 67 patients were ultimately diagnosed with cancer including 14 (21%) liver, 45 (67%) lung, and 8 (12%) pancreatic cancers. This demonstrates an incidence of 0.2% new cancer diagnoses among all validated reports and 15% incidence among navigated patients. CONCLUSIONS: AI technology identified high-risk lesions in approximately 5% of patients reviewed, which would have taken significantly more time to be worked up by traditional means or might have been missed altogether. The benefits for patients may include decreased time to diagnosis and increased rates of follow-up with specialty care. The machine learning algorithms and validation will enable improved model performance by modifying the variables entered. This project highlights that AI technology has vast potential for streamlined patient care and helps support multidisciplinary teams in ensuring that actionable findings are appropriately identified and managed. AI technology may fundamentally change the ability of patients to receive prompt and efficacious cancer care.

Abstract as published, via PubMed.