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Artificial Intelligence-Driven Drafting of Chest X-Ray Reports: 2025 Position Statement From the Korean Society of
Won Gi Jeong1,2, Eui Jin Hwang3, Gong Yong Jin4
1Department of Radiology, National Cancer Center, Goyang, Republic of Korea.
Objective:
Generative artificial intelligence (AI) systems can be used to draft automated chest X-ray (CXR) reports. Although promising in terms of efficiency and workforce shortages, their accuracy, reliability, and clinical utility remain uncertain. This article presents the Korean Society of Thoracic Radiology (KSTR) position statement on AI-assisted CXR report drafting, derived from a Delphi survey of experts who used the software on a modest case set.
Materials And Methods:
Twenty thoracic radiologists completed a Delphi survey after reviewing 60 CXR cases using an AI-based tool for automated report drafting (KARA-CXR, version 1.0.0.3; KakaoBrain, Seoul, Republic of Korea). Prior to the Delphi survey, the participants individually reviewed 60 CXR cases at their respective workplaces as part of the survey preparation process. The 60 cases were distributed evenly across six clinical settings (health screening, inpatient, emergency department, intensive care unit, respiratory outpatient, and non-respiratory outpatient), with 10 cases in each setting. The participants individually selected CXR cases in which they had worked. The entire selection and review processes were completed within 1 month. Subsequently, two Delphi rounds were conducted. Participants rated 12 key questions (72 items) regarding the clinical applicability of the AI-based tool on a 9-point Likert scale. Consensus required ≥70% agreement.
Results:
Consensus emerged for 41 of 72 items (56.9%). Respondents adopted a neutral stance on most questions concerning accuracy and clinical integration; they were neither impressed nor disappointed with the tool. A favorable view emerged only for health-screening examinations. Conversely, the stand-alone use of the AI-based tool in routine practice was opposed. Participants stressed the need for further performance optimization before deployment and advocated society-endorsed education and guidelines before adoption.
Conclusion:
The KSTR supports the use of an AI-based automated CXR report-drafting tool only in health-screening settings with radiologist validation and opposes its standalone use in routine practice, recommending performance optimization and society-endorsed education and guidelines before its adoption.
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