AI for the Full Lung Cancer Journey, From Patient Identification to Diagnosis
We help Community Health Centers (FQHCs) close screening gaps (e.g., breast, lung, colorectal, and annual wellness visits) and provide radiologists with reimbursable AI lung cancer screening diagnostics (CADx) embedded into your current PACS/CADe.
The U.S. Is Failing at Cancer Screening and Early Detection
Cancer affects every family: around one-third of us will face a diagnosis, and one-half will lose their life to the disease.
Low Screening Rates
Fewer than 18% of eligible patients receive lung cancer screening, and at FQHCs, that number drops below 6%
Late-Stage Diagnoses Dominate
85% of lung cancer cases are detected too late for effective intervention.
Limited Uptake of Billable AI
Imaging centers lack CPT-reimbursable AI tools to improve diagnostic yield.
Access Gaps Are Widening
FQHCs and rural health systems are under-resourced, understaffed, and overwhelmed.
Late Diagnoses Are Financially Devastating
Failing to detect lung cancer early results in over $150 billion in annual healthcare spending
AI-Powered Lung Cancer Screening Workflow
From patiaent identification, education, to diagnosis
Current State of LungRADS v2022
LUNG–RADS V2022 is currently the standard of care for predicting lung malignancy on low-dose CT, but it has a 9% false-negative and 18%+ false-positive rate.
There is an urgent need for improved, data-driven, interpretable approaches to early lung cancer risk stratification
