Australia’s launch of a National Lung Cancer Screening (NLCS) Program in July 2025 marks a turning point in the country’s fight against its leading cause of cancer death. With early-stage detection rates historically low, the program targets high-risk populations with low-dose CT (LDCT) scans to catch cancers earlier - when they’re more treatable.
But population-scale screening brings population-scale challenges. Screening tens of thousands of scans annually means analyzing a high volume of complex CTs consistently and quickly. This is where artificial intelligence in radiology becomes not a luxury but a requirement.
The NELSON trial in Europe showed a 24% reduction in lung cancer mortality with LDCT. Korea’s national program, launched after a successful pilot, now screens tens of thousands annually - relying heavily on AI to support triage, reporting, and follow-up.
To replicate this success, Australia will need more than scanners and guidelines. It will need infrastructure that makes AI in lung cancer screening work across public and private systems, radiology groups, and PACS environments.
CARPL acts as a unified AI platform and an orchestration layer, connecting hospitals to multiple validated radiology AI solutions that analyse and advance early detection in LDCTs. CARPL builds customized workflows for LCS that allow autopopulation of PANCAN scores and support longitudinal tracking of integral diagnostic markers like nodule number, location, and size, among many others.
Rather than integrating separate AI solutions, HCPs and PACS vendors work through CARPL’s unified interface to access and validate FDA and TGA-cleared AI solutions that best fit their population and workflow, all while receiving continuous clinical and technical support.
For healthcare providers, this means reduced reporting variability and faster turnaround. For PACS and modality vendors, CARPL eliminates complex one-off integrations. For AI developers, it means scalable access to real-world data and deployment environments.
Australia’s screening program is a public health initiative, but it needs system-level innovation to succeed. By enabling AI in medical imaging to operate at scale, CARPL helps ensure this program delivers what it promises: earlier detection, equitable access, and better outcomes.
