Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis
The AI system enhanced lung nodule detection, offering the possibility of earlier disease identification and timely intervention. Nevertheless, the initial reduction in accuracy underscores the need for standardized diagnostic criteria and comprehensive training for radiologists to maximize the effectiveness of AI-enabled diagnostic systems.
Authors
- You Wu (ORCID: https://orcid.org/0000-0001-9672-6129)
- Zhuozhao Zheng (ORCID: https://orcid.org/0000-0001-8547-6670)
- W Liu (ORCID: https://orcid.org/0009-0002-6221-7136)
- Mark J. Bittle (ORCID: https://orcid.org/0000-0001-7258-4445)
- Hadi H.K. Kharrazi (ORCID: https://orcid.org/0000-0003-1481-4323)
- Wei Yu (ORCID: https://orcid.org/0000-0001-9526-4559)
Institutions
- Johns Hopkins University (US)
- Capital Medical University (CN)
- Beijing Founder Electronics (China) (CN)
- Beijing Anzhen Hospital (CN)
- Beijing Tsinghua Chang Gung Hospital (CN)
- Beijing Hua Xin Hospital (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-01-27
- DOI
- https://doi.org/10.2196/64649
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.80