Artificial Intelligence: Can It Save Lives, Hospitals, and Lung Screening?

Background: Early detection is essential in lung cancer survival. Lung screening or incidental detection on unrelated imaging holds the most promise for early detection. With the large volume of imaging performed today, management of incidental pulmonary nodules can be difficult. We hypothesized an artificial intelligence (AI) tool could reliably read all imaging reports, detect, and effectively triage indeterminate pulmonary nodules without adding additional personnel, helping save lives.

Methods: An incidental lung nodule clinic (ILNC) was created using AI and an existing nurse practitioner. Over 26 months, the software read all radiology reports, visualizing any lung tissue. Patients with nodules >3 mm and considered indeterminate by the nurse practitioner were referred to the ILNC. High-risk patients with benign nodules were offered entry into the lung screening program.

Results: Of 502,632 imaging reports analyzed, 22,136 (4.4%) had positive findings. Follow-up data were lacking in 11,797 (2.3%), 911 (7.7%) were verified lost, and 518 (4.4%) were referred to the ILNC. There were 393 patients with benign nodules and accepted enrollment in the lung screening program. Mean age of enrolled patients was 61 years, and 53% were men. Workup included 499 diagnostic computed tomographic scans, 39 positron emission tomographic scans, and 27 biopsy samples that identified 15 malignancies (2.9%), with 14 lung cancers (8 stage I, 4 stage III, and 2 stage IV). Treatment included 5 lobectomies, and 4 underwent stereotactic body radiation therapy. Financials were favorable.

Conclusions: AI software can supplement practitioners, help diagnose lung cancer earlier, save lives, and generate value-based revenue for the hospital.