A Q&A with Dr. Ryoungwoo Jang, Clinical Research LeadCoreline Soft is passionate about developing AI imaging solutions that make a real difference for patients. Operating under the slogan “Staying Ahead of Symptoms,” the mission of this South Korea-based company is to help healthcare professionals around the world detect diseases before symptoms even occur. With only a single chest CT scan, Coreline Soft’s medical AI solutions can identify various findings – such as lung nodules, emphysema and interstitial lung abnormalities – enabling earlier diagnosis and treatment of lung diseases. We spoke with Dr. Ryoungwoo Jang, the company’s clinical research lead, about how its AI-powered CT solutions are advancing early lung disease detection and clinical decision support. Below is an excerpt from our conversation.
Tell us about Coreline Soft’s latest developments.
We’ve developed a novel concept, estimated forced vital capacity (eFVC), for interstitial lung disease (ILD). eFVC allows us to quantify a patient’s radiologic lung status and directly influence care. We aim to demonstrate that eFVC is non-inferior to measured FVC and to eliminate the one-year follow-up currently required to confirm progressive pulmonary fibrosis (PPF). Ultimately, we hope to enable simultaneous diagnosis and treatment of ILD and PPF while improving prognostic prediction.
What else are you doing that’s making a difference in the lung disease space?
In an era when AI is widely regarded as the future of healthcare, technology alone is not enough. Medical AI, which deals directly with human lives, must meet higher standards — accuracy is essential, but so are regulation, ethics and transparency. At Coreline Soft, we are redefining clinical trust through strategic regulatory readiness and next-generation lung cancer screening pathways.
Why did you join OSIC?
As a chest AI imaging company, we understand the value and importance of high-quality data. We greatly appreciate OSIC and the invaluable dataset you are providing. It is making a big impact on the ILD research community.
What have you been able to accomplish by working with the OSIC Cloud Data Repository?
We developed eFVC with the OSIC database as a training set — something that wouldn’t have been possible without that data. We look forward to continuing our collaboration with OSIC to explore further ways of applying our algorithm to help patients with chest diseases. |