Measuring the Impact of AI on Report-Drafting Efficiency in Chest Computed Tomography Interpretation: Retrospective Analysis
AI-assisted lung nodule diagnosis may initially increase report-drafting time due to adaptation and workflow adjustment. Sustained, meaningful efficiency gains were heterogeneous and observed at only 1 of the 2 study sites, indicating that long-term impacts are strongly contingent on site-specific implementation dynamics, learning curves, and local context.
Authors
- 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 Kharrazi
- Wei Yu
- You Wu
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
- 2026-02-14
- DOI
- https://doi.org/10.2196/77967
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00