Cited by 25

Artificial intelligence for low-dose CT lung cancer screening: Comparison of utilization scenarios

Lee, Meesun, Hwang, Eui Jin, Lee, Jong Hyuk, Nam, Ju Gang, Lim, Woo Hyeon, Park, Hyungin, Park, Chang Min, Choi, Hyewon +2 more·2025·American Journal of Roentgenology

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Registry

Crossref · 10.2214/ajr.25.32829

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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  • Using AI as a prescreener, where radiologists only review AI-positive scans, can reduce workload and maintain sensitivity, while AI as an assistant or backup may increase recall rates but also false positives and interpretation time.

    “Implementation scenarios vary: using AI as a prescreener (where radiologists only review AI-positive scans) can reduce workload and maintain sensitivity, while AI as an assistant or backup may increase recall rates but also false positives and interpretation time [17].”

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Authors
Lee, M., Hwang, E. J., Lee, J. H., Nam, J. G., Lim, W. H., Park, H., Park, C. M., Choi, H., Park, J., & Goo, J. M.
Affiliations
  • Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea.
  • Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Department of Radiology, Inje University Haeundae Paik Hospital, Busan, Republic of Korea.
  • Department of Radiology, Chung-Ang University Hospital, Chung-ang University College of Medicine, Seoul, Republic of Korea.
  • Department of Radiology, Yeungnam University Medical Center, Daegu, Republic of Korea.
Venue
American Journal of Roentgenology
Published
2025-07
Volume
225
Issue
1
ISSN
  • 0361-803X
  • 1546-3141
Cited by
25
References
1
Language
en

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