Korea IT Times celebrates its 20th anniversary with Insightful columns from local and international thought leaders. Following contributions from experts across various fields in July, August, September, October, and November, we now introduce this December's column.
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Jin Hyung Kim, Emeritus Professor at KAIST: "AI Jesus Speaking 100 Languages, Miracle or Threat?"
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Jong-Shik Kim, Chair Professor of aSSIST University: "An Aging Society and Startups"
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Emanuel Pastreich, President of The Asia Institute: "Yoon Fights Dirty, Are Koreans Ready?"
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Sven Lindström, Executive VP, Midsummer: "Solar Energy Trends in 2025"
By Prof. Wonki Jeong
In 2019, South Korea set a new milestone in cancer prevention by becoming the first country in the world to introduce national lung cancer screening using low-dose chest CT as part of its national cancer screening program. This program focuses on early diagnosis and treatment to reduce mortality rates from lung cancer, which remains a leading cause of high incidence and mortality globally, particularly among the elderly population. If this program is successfully established in South Korea, which is undergoing rapid population aging, it could not only significantly reduce social costs but also greatly contribute to public health promotion. The core of the national lung cancer screening lies in the interpretation of low-dose chest CT scans. The role of radiology specialists in this process is crucial, yet the current shortage of expert manpower remains a practical challenge. Fortunately, advancements in medical imaging artificial intelligence (AI) offer the possibility of overcoming these limitations.
National Lung Cancer Screening: Potential and Challenges of Medical Imaging AI
Implementing AI in lung cancer screening can offer several advantages. First, it can enhance diagnostic accuracy by automatically detecting and evaluating major lesions, including lung nodules. Second, it allows for the quantitative analysis of various important findings seen on CT images, such as emphysema, interstitial lung abnormalities, coronary artery calcification, and thoracic aortic diseases, efficiently handling complex tasks that are difficult for humans. Additionally, this technology is useful for sharing research outcomes of national lung cancer screening with the global medical community. AI-driven data and research results can serve as valuable reference materials for setting international standards and extending the program.
The New Role of Radiology and AI's Contribution
In a time when healthcare faces significant challenges, radiology is entering an era where it must prove new values beyond generating additional revenue. One of the main causes is the rapidly aging population versus the limited available medical resources. While quantitative expansion of imaging tests was feasible in the past, now it must justify its existence through qualitative improvement within these limited resources. In this process, the contribution of medical imaging AI is gaining attention. First, AI can be utilized as an effective means to assist medical professionals by reducing errors and improving accuracy while also addressing the issue of insufficient medical staff. Second, AI can swiftly and efficiently handle quantitative analysis tasks that are difficult or time-consuming for humans to perform. For example, findings such as emphysema or interstitial lung abnormalities in chest CT images are complex to analyze, but AI can perform these tasks quickly and accurately. Third, AI plays a critical role in triaging severe patients, including in emergency situations, clearly identifying areas that require prompt intervention by medical staff, and enabling immediate response.
Challenges of Utilizing Medical AI and the Role of Associations
However, to effectively implement AI technology in clinical practice, the role of medical associations is vital. The Korean Society of Thoracic Radiology (President Jin Gong-yong) is helping medical professionals effectively utilize AI technology by providing AI-related education to its members and fostering cooperation with development companies. These efforts by the association can create a virtuous cycle between medical professionals and developers, opening the potential to turn the Korean healthcare crisis into an opportunity.
Conclusion
Rapid aging and limited medical resources are significant challenges faced by modern healthcare systems. Medical imaging AI is expected to establish itself as one of the solutions to overcome these challenges. If AI technology is successfully incorporated into clinical practice and research, it could open new possibilities across the healthcare field. We look forward to witnessing Korea's challenges and innovations in transforming crisis into opportunity in healthcare.
