ArticleAcademic radiology2020
Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence.
Article in Academic radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review on the impact of deep learning-driven worklist triage on radiology workflow and clinical outcomes.European radiology · 2025Pooled it
- Application of artificial intelligence in X-ray imaging analysis for knee arthroplasty: A systematic review.PloS one · 2025Pooled it
- Skeletal muscle radiodensity as an automated survival predictor in men with metastatic castration-resistant prostate cancer undergoing PSMA-targeted radioligand therapy.European journal of nuclear medicine and molecular imaging · 2026Article
- Artificial Intelligence in Lung Cancer: From Early Detection to Personalized Therapy.Current oncology reports · 2026Review
- Implementing an Artificial Intelligence Decision Support System in Radiology: Prospective Qualitative Evaluation Study Using the Nonadoption Abandonment Scale-Up, Spread, and Sustainability (NASSS) Framework.Journal of medical Internet research · 2026Article
- Radiologist, Trainee, and Logistical Factors Impacting the Timeliness of CTA Head and Neck Reporting in Stroke Code Activations.AJNR. American journal of neuroradiology · 2025Observational
- Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and Cascaded DualUNet.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- Development of an oral cancer detection system through deep learning.BMC oral health · 2024Article
- Selecting the Best Radiology Workflow Efficiency Applications.Journal of imaging informatics in medicine · 2024Article
- Enhancing Efficiency with an AI-Augmented Clinician in Neurology.Aging and disease · 2024Article
- Validating the accuracy of deep learning for the diagnosis of pneumonia on chest x-ray against a robust multimodal reference diagnosis: a post hoc analysis of two prospective studies.European radiology experimental · 2024Article
- Development and External Validation of an Artificial Intelligence-Based Method for Scalable Chest Radiograph Diagnosis: A Multi-Country Cross-Sectional Study.Research (Washington, D.C.) · 2024Article
- Are the Pilots Onboard? Equipping Radiologists for Clinical Implementation of AI.Journal of digital imaging · 2023Article
- Understanding the use of artificial intelligence for implant analysis in total joint arthroplasty: a systematic review.Arthroplasty (London, England) · 2023Review
- Translation of AI into oncology clinical practice.Oncogene · 2023Review
- Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges?Cancers · 2023Review
- Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis.Frontiers in sports and active living · 2023Article
- Artificial Intelligence in Emergency Radiology: Where Are We Going?Diagnostics (Basel, Switzerland) · 2022Review
- Developing an Artificial Intelligence Model for Reading Chest X-rays: Protocol for a Prospective Validation Study.JMIR research protocols · 2022Article
- Interactive Multimedia Reporting Technical Considerations: HIMSS-SIIM Collaborative White Paper.Journal of digital imaging · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
RATIONALE AND
objectivesOur primary aim was to improve radiology reports by increasing concordance of target lesion measurements with oncology records using radiology preprocessors (RP). Faster notification of incidental actionable findings to referring clinicians and clinical radiologist exam interpretation time savings with RPs quantifying tumor burden were also assessed. MATERIALS AND
methodsIn this prospective quality improvement initiative, RPs annotated lesions before radiologist interpretation of CT exams. Clinical radiologists then hyperlinked approved measurements into interactive reports during interpretations. RPs evaluated concordance with our tumor measurement radiologist, the determinant of tumor burden. Actionable finding detection and notification times were also deduced. Clinical radiologist interpretation times were calculated from established average CT chest, abdomen, and pelvis interpretation times.
resultsRPs assessed 1287 body CT exams with 812 follow-up CT chest, abdomen, and pelvis studies; 95 (11.7%) of which had 241 verified target lesions. There was improved concordance (67.8% vs. 22.5%) of target lesion measurements. RPs detected 93.1% incidental actionable findings with faster clinician notification by a median time of 1 hour (range: 15 minutes-16 hours). Radiologist exam interpretation times decreased by 37%.
conclusionsThis workflow resulted in three-fold improved target lesion measurement concordance with oncology records, earlier detection and faster notification of incidental actionable findings to referring clinicians, and decreased exam interpretation times for clinical radiologists. These findings demonstrate potential roles for automation (such as AI) to improve report value, worklist prioritization, and patient care.
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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.