Coreline Soft’s medical imaging AI solution, AVIEW, is expanding its footprint across global healthcare settings, supported by a growing body of clinical evidence. The number of studies and publications based on the platform has recently exceeded 500, highlighting a broader shift in how medical AI is being used in real-world clinical and research environments.
The milestone is particularly notable as the data has been accumulated across multiple countries and healthcare institutions, rather than being confined to a single organization or project. As clinicians and researchers repeatedly use the same analytical framework, a level of standardization in quantitative imaging analysis is beginning to emerge.
One of the most significant developments in recent research is the evolving role of AI. While earlier applications focused primarily on detecting abnormalities, current studies increasingly emphasize risk stratification—quantifying disease progression and categorizing patient risk levels. This reflects a transition from AI as a diagnostic aid to a tool supporting prognosis and long-term patient management.
Global research trends reinforce this shift. European studies have shown that AI solutions can be effectively used not only in initial diagnosis but also in follow-up assessments over extended periods. In addition, lung cancer screening projects in Italy and the United Kingdom have reported substantial reductions in radiologists’ workload, suggesting that AI is moving beyond assistive functions to influence broader operational workflows in healthcare systems.
AVIEW is currently deployed in 19 countries and has been used in more than 2.5 million clinical readings, generating a large volume of real-world data. Such data is considered essential for narrowing the gap between controlled research outcomes and actual clinical practice, strengthening the long-term viability of AI technologies in medicine.
Coreline Soft is also expanding its role in national-level screening programs across Europe, including lung cancer initiatives. In these settings, AI is no longer limited to improving diagnostic accuracy but is increasingly integrated into system-level functions such as multi-center data standardization and screening process management.
These developments are reshaping the business models of medical AI companies. The industry is gradually moving away from one-time licensing toward recurring revenue structures linked to actual usage. In large-scale screening programs, securing integration within national healthcare systems may prove more critical than initial sales, as ongoing usage naturally drives long-term revenue.
Industry observers view this transition as a turning point, with medical AI evolving from a product-based market to an infrastructure-driven model. The key competitive factor is shifting from technological performance alone to how effectively AI can be embedded into healthcare systems and sustained over time.
Surpassing 500 clinical studies, therefore, represents more than a numerical milestone. It underscores a broader transformation in which medical AI is no longer a supplementary tool, but an integral component of diagnosis, prognosis, and healthcare operations.
