ArticleNPJ digital medicine2023
The impact of artificial intelligence on the reading times of radiologists for chest radiographs.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Factors influencing AI acceptance in radiology: a systematic review across the radiology workflow.European radiology · 2026Pooled it
- Primary Care Doctors' Perspectives and Experiences With a Chest X-Ray AI Triage Program: Qualitative Study.Journal of medical Internet research · 2026Article
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Impact of AI-Triaged Worklists and AI-Assisted Report Generation on Radiology Turnaround Times: Prospective Real-World Study.Journal of medical Internet research · 2026Article
- Impact of AI assistance on reading time, cancer detection rate, and abnormal interpretation rate in screening and diagnostic mammography: a prospective alternating-month study.European radiology · 2026Article
- Learning neuroimaging models from health system-scale data.Nature biomedical engineering · 2026Article
- Commercial artificial intelligence-assisted performance and interpretation time of first-on-call radiology residents using computed tomography pulmonary angiography to detect pulmonary embolism: a multireader, multicenter study.Quantitative imaging in medicine and surgery · 2026Article
- Plasmonic artificial inspector for herbal medicines via surface-enhanced Raman spectroscopy and deep learning.Scientific reports · 2026Article
- The Effect of AI on the Radiologist Workforce: A Task-Based Analysis.medRxiv : the preprint server for health sciences · 2025Article
- Evaluation of health professionals' perceptions on the use of artificial intelligence in radiology: a questionnaire-based study.BMC medical education · 2025Article
- Automated Midline Shift Detection in Head CT Using Localization and Symmetry Techniques Based on User-Selected Slice.AJNR. American journal of neuroradiology · 2025Article
- The Impact of Artificial Intelligence on Radiologists' Reading Time in Bone Age Radiograph Assessment: A Preliminary Retrospective Observational Study.Journal of imaging informatics in medicine · 2025Observational
- External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal Radiographs: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Crucial Role of Understanding in Human-Artificial Intelligence Interaction for Successful Clinical Adoption.Korean journal of radiology · 2025Article
- Learning neuroimaging models from health system-scale data.Research square · 2025Article
- The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection.Frontiers in medicine · 2025Review
- AI-assisted radiologists vs. standard double reading for rib fracture detection on CT images: A real-world clinical study.PloS one · 2025Article
- Artificial Intelligence interpretation of chest radiographs in intensive care. Ready for prime time?Intensive care medicine · 2025Article
- Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points.Scientific reports · 2024Article
- Accuracy of radiologists and radiology residents in detection of paediatric appendicular fractures with and without artificial intelligence.BMJ health & care informatics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Whether the utilization of artificial intelligence (AI) during the interpretation of chest radiographs (CXRs) would affect the radiologists' workload is of particular interest. Therefore, this prospective observational study aimed to observe how AI affected the reading times of radiologists in the daily interpretation of CXRs. Radiologists who agreed to have the reading times of their CXR interpretations collected from September to December 2021 were recruited. Reading time was defined as the duration in seconds from opening CXRs to transcribing the image by the same radiologist. As commercial AI software was integrated for all CXRs, the radiologists could refer to AI results for 2 months (AI-aided period). During the other 2 months, the radiologists were automatically blinded to the AI results (AI-unaided period). A total of 11 radiologists participated, and 18,680 CXRs were included. Total reading times were significantly shortened with AI use, compared to no use (13.3 s vs. 14.8 s, p < 0.001). When there was no abnormality detected by AI, reading times were shorter with AI use (mean 10.8 s vs. 13.1 s, p < 0.001). However, if any abnormality was detected by AI, reading times did not differ according to AI use (mean 18.6 s vs. 18.4 s, p = 0.452). Reading times increased as abnormality scores increased, and a more significant increase was observed with AI use (coefficient 0.09 vs. 0.06, p < 0.001). Therefore, the reading times of CXRs among radiologists were influenced by the availability of AI. Overall reading times shortened when radiologists referred to AI; however, abnormalities detected by AI could lengthen reading times.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.