Taiwan’s lung cancer screening market is rapidly evolving from a race focused on AI image interpretation to a broader competition centered on how large-scale screening systems are designed, managed, and operated.
The shift comes as Taiwan considers expanding its national low-dose CT (LDCT) screening policy beyond traditional high-risk smokers, driven by a growing number of lung cancer cases among non-smokers.
Taiwan was among the earlier Asian countries to introduce LDCT-based lung cancer screening programs. Until now, the system has largely targeted high-risk groups with smoking histories. However, medical experts and healthcare institutions are increasingly discussing whether screening should be expanded to include the general population.
Several leading Taiwanese hospitals, including National Taiwan University Hospital (NTUH) and Chang Gung Memorial Hospital, are reportedly conducting pilot studies involving non-smokers. Industry observers say the discussions could eventually lead to changes in national screening guidelines.
As screening populations expand, hospitals are facing a new set of operational challenges. Under smaller screening programs, radiologist-centered interpretation systems were manageable. But large-scale nationwide screening raises concerns about workflow efficiency, staffing, data integration, and long-term patient management.
Healthcare providers are increasingly focusing on areas such as high-volume reading systems, standardized reporting, follow-up management, inter-hospital data connectivity, and quality control.
Industry experts say the core issue in lung cancer screening is shifting from “how accurately AI can read images” to “how effectively the entire screening system can be operated.”
Against this backdrop, South Korean medical AI company Coreline Soft is expanding its presence in Taiwan’s healthcare market.
The company has reportedly supplied AI screening solutions to more than 60 hospitals out of roughly 200 Taiwanese medical institutions considered potential adopters of AI lung screening systems.
Its client network includes several of Taiwan’s leading medical centers, including NTUH, Chang Gung Memorial Hospital, Taipei Medical University, and Kaohsiung Medical University.
Industry analysts note that policy-driven medical AI markets often follow a familiar pattern. Initial pilot-stage deployments generate limited revenue, but once national screening programs expand, adoption accelerates rapidly and recurring revenue structures begin to emerge.
Coreline Soft’s recent upgrade sales tied to its “AVIEW LCS+ 2.0” platform are being viewed as an important signal within the industry. Rather than switching to competing systems, existing hospital clients are upgrading to higher-tier versions within the same platform ecosystem.
Analysts say this suggests switching costs are beginning to take hold while also indicating the potential emergence of a long-term SaaS-style recurring revenue model.
At the same time, Taiwan’s AI healthcare market is becoming increasingly competitive as local AI vendors enter the field and pricing pressure intensifies. While competition around standalone image-reading algorithms is growing, demand for integrated operational management capabilities is expanding even faster.
Coreline Soft has been broadening its focus beyond image interpretation alone, incorporating multi-disease analysis, structured reporting, and long-term follow-up management into its platform strategy.
Industry attention is also turning toward possible collaborations between medical AI firms and global pharmaceutical companies. Some early-stage discussions are reportedly exploring “patient finding” models that use screening data to identify patients earlier and connect them with treatment pathways.
“The Taiwanese lung cancer screening market is no longer simply about AI technology itself,” one industry official said. “The real competitive advantage is becoming the ability to operate and sustain large-scale screening systems efficiently. In the future, operational capability may matter more than algorithm performance alone.”
