Artificial Intelligence-Assisted Pulmonary Nodule Diagnosis by Thoracic Surgeons: A Comparative Study on Clinical Effectiveness.

Yeong Jeong Jeon¹, Jonghun Jeong^2,3, Doohyun Park², Jung-Hyun Kang², Eun Kyoung Hong⁴, Younjoon Chung⁵, Boram Park⁶, Seong Yong Park¹

¹Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

²Vuno Inc., Seoul, Republic of Korea.

³Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.

⁴Department of Radiology, Brigham & Women's Hospital, Boston, Massachusetts, USA.

⁵Department of Computer Science, Yale School of Engineering and Applied Science, Yale University, New Haven, Connecticut, USA.

⁶College of Medicine, Inha University, Incheon, Republic of Korea.

PubMed PMC DOI PDF

Abstract

MAIN PROBLEM: Clinician experience variability affects lung cancer detection using computed tomography. AI-based computer-aided diagnosis (CAD) systems can improve diagnostic accuracy, but their influence on thoracic surgeons with varying experience is unclear. This study aimed to evaluate the impact of CAD system on thoracic surgeons' assessment of pulmonary nodule malignancy. METHODS: A multire…

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