ArticleInsights into imaging2024
A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence-Enabled Imaging for Predicting Preoperative Extraprostatic Extension in Prostate Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Radiology reporting in the age of artificial intelligence: implications for patient safety.BMJ quality & safety · 2026Article
- AI as Core Infrastructure in Radiology: Moving Beyond Pilots to Operational Excellence.Journal of imaging informatics in medicine · 2026Article
- Integrating artificial intelligence into paediatric interventional radiology: a review of emerging applications and future directions.Pediatric radiology · 2026Review
- The AI implementation gap in trauma radiography: standalone versus discretionary AI-integrated fracture detection.European radiology experimental · 2026Article
- Lightweight hybrid foundation model for lung cancer prognosis based on low-dose chest X-ray images.Translational lung cancer research · 2026Article
- Critical dialogue: how imaging evaluates and guides pelvic exenteration surgery-a multidisciplinary perspective.Insights into imaging · 2026Article
- Artificial intelligence improves radiologist workflow and assessment of image quality accuracy.Bioinformation · 2026Article
- Deep Learning Framework for Automated MRI Planimetry in Multiple Sclerosis.International journal of biomedical imaging · 2026Article
- AI in radiology and interventions: a structured narrative review of workflow automation, accuracy, and efficiency gains of today and what's coming.International journal of computer assisted radiology and surgery · 2026Review
- [Artificial intelligence in musculoskeletal radiology : Current advances and emerging frontiers].Orthopadie (Heidelberg, Germany) · 2026Article
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Evaluating the role of large language models in supporting patient education during the informed consent process for routine radiology procedures.The British journal of radiology · 2025Article
- Article
- Automated Integration of AI Results into Radiology Reports Using Common Data Elements.Journal of imaging informatics in medicine · 2025Article
- Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.Bioengineering (Basel, Switzerland) · 2025Review
- Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach.Discover oncology · 2025Review
- SCassist: an AI based workflow assistant for single-cell analysis.Bioinformatics (Oxford, England) · 2025Article
- Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow EfficiencyCurrent medical imaging · 2025Review
- Academic Pursuits in Teleradiology: At the Crossroads of Technology, Radiology, and Implementation.The Indian journal of radiology & imaging · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesArtificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools.
methodsFindings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports.
resultsChest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001).
conclusionThe AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future. CRITICAL RELEVANCE STATEMENT: With the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest. KEY POINTS: • A pipeline was developed for automated transfer of AI results into structured reports. • Pipeline chest X-ray reporting is faster than free-text or conventional structured reports. • Report quality was also rated higher for reports created with the pipeline. • The pipeline offers efficient, standardized AI integration into the clinical workflow.
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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 Socratic graph.