Evaluating artificial intelligence’s role in lung nodule diagnostics: A survey of radiologists in two pilot tertiary hospitals in China
Radiologists at two leading Beijing hospitals generally perceive the AI-assisted lung nodule diagnostic system positively, citing its user-friendliness and effectiveness. However, the system's learnability requires enhancement. While AI is seen as beneficial for work efficiency and diagnostic accuracy, its long-term career implications remain a topic of debate.
Authors
- You Wu (ORCID: https://orcid.org/0000-0001-9672-6129)
- Zhuozhao Zheng (ORCID: https://orcid.org/0000-0001-8547-6670)
- Mark J. Bittle (ORCID: https://orcid.org/0000-0001-7258-4445)
- Hadi H.K. Kharrazi (ORCID: https://orcid.org/0000-0003-1481-4323)
- Weiqi Liu (ORCID: https://orcid.org/0000-0001-5602-7841)
- Wei Yu (ORCID: https://orcid.org/0000-0001-9526-4559)
Institutions
- Johns Hopkins University (US)
- Capital Medical University (CN)
- Beijing Anzhen Hospital (CN)
- Beijing Tsinghua Chang Gung Hospital (CN)
- Beijing Hua Xin Hospital (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Journal of Clinical Imaging Science
- Published
- 2024-08-23
- DOI
- https://doi.org/10.25259/jcis_72_2024
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.62