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REVOLUTIONIZING LUNG CANCER DIAGNOSIS: THE ROLE OF AI IN EARLY DECTION AND HEALTHCARE SAVINGS
K. Adam Lee, Lindsay Silas.
Jupiter Medical Center, jupiter, FL, USA.
OBJECTIVE:Artificial intelligence is transforming lung cancer care by enabling earlier detection through advanced imaging analysis. Tools like AI platforms improve diagnostic accuracy, reduce unnecessary procedures, and offer significant cost savings, supporting better outcomes in a value-based healthcare system.
METHODS:An FDA-approved AI platform was deployed in our lung nodule clinic over 13 months to automate patient identification, tracking, and risk stratification with a Lung Cancer Predictor (LCP) score. We assessed patient volume from clinicians and self-referrals, Nodule Clinic Board LCP requests, and incidental Emergency Department nodules. Outcomes measured included follow-up imaging, biopsies, surgeries, and return on investment based on Florida Medicare rates, and estimated profit margins on operative procedures.
RESULTS:136 new patients referred for AI lung nodule interpretation were analyzed, all showing positive findings and enrolled into the lung nodule clinic. Diagnostic procedures included 74 follow-up CT scans and 30 PET/CT scans. 25 patients underwent robotic bronchoscopy biopsies and 16 had transthoracic biopsies. 24 proceeded to surgery, resulting in 8 lobectomies and 16 segmentectomies and one bronchoscopic ablation. 75% were stage I malignancies, and 16 patients relegated to stereotactic body radiation. Based on 2024 Medicare rates for Palm Beach County, Fl, these patients generated an estimated $584327.76 in additional revenue averaging $50,000 per month for the thoracic surgery program.
CONCLUSIONS:The application of a clinically validated, FDA-approved AI platform improved lung cancer detection, enhanced diagnostic accuracy, reduced unnecessary procedures, increased cost efficiency, and validated a strong return on investment, supporting cost-effectiveness and value-based care.
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