ArticleJournal of medical imaging (Bellingham, Wash.)2020
Integrating AI into radiology workflow: levels of research, production, and feedback maturity.
Article in Journal of medical imaging (Bellingham, Wash.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- LLaMA-XR: A Novel Framework for Radiology Report Generation Using LLaMA and QLoRA Fine Tuning.Bioengineering (Basel, Switzerland) · 2026Article
- State and Diffusion of National Institutes of Health Funding of AI in Radiology.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
- 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
- Deep Learning Framework for Automated MRI Planimetry in Multiple Sclerosis.International journal of biomedical imaging · 2026Article
- Radiology Staff Experiences With Integrating Artificial Intelligence Into Radiology Practice in a Swedish Hospital: Qualitative Case Study.JMIR formative research · 2025Article
- Are Image-Based Deep Learning Algorithms of Kidney Volume in Polycystic Kidney Disease Ready for Clinical Deployment? A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Real-World Monitoring of Artificial Intelligence in Radiology: Challenges and Best Practices.Korean journal of radiology · 2025Review
- Progress in the application of machine learning in CT diagnosis of acute appendicitis.Abdominal radiology (New York) · 2025Review
- Boosting LLM-assisted diagnosis: 10-minute LLM tutorial elevates radiology residents' performance in brain MRI interpretation.Neuroradiology · 2025Article
- Evolution of an Artificial Intelligence-Powered Application for Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Cross-institutional automated multilabel segmentation for acute intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT.Radiology advances · 2025Article
- Artificial intelligence for cardiac imaging is ready for widespread clinical use: Pro Con debate AI for cardiac imaging.BJR open · 2025Review
- Choosing the right artificial intelligence solutions for your radiology department: key factors to considerDiagnostic and interventional radiology (Ankara, Turkey) · 2024Review
- Review
- Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.Radiology. Artificial intelligence · 2024Article
- Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024.Diagnostics (Basel, Switzerland) · 2024Review
- The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century.Bioengineering (Basel, Switzerland) · 2024Review
- Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.Insights into imaging · 2024Article
- Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA.Radiology. Artificial intelligence · 2024Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
We present a roadmap for integrating artificial intelligence (AI)-based image analysis algorithms into existing radiology workflows such that (1) radiologists can significantly benefit from enhanced automation in various imaging tasks due to AI, and (2) radiologists' feedback is utilized to further improve the AI application. This is achieved by establishing three maturity levels where (1) research enables the visualization of AI-based results/annotations by radiologists without generating new patient records; (2) production allows the AI-based system to generate results stored in an institution's picture-archiving and communication system; and (3) feedback equips radiologists with tools for editing the AI inference results for periodic retraining of the deployed AI systems, thereby allowing continuous organic improvement of AI-based radiology-workflow solutions. A case study (i.e., detection of brain metastases with T1-weighted contrast-enhanced three-dimensional MRI) illustrates the deployment details of a particular AI-based application according to the aforementioned maturity levels. It is shown that the given AI application significantly improves with feedback coming from radiologists; the number of incorrectly detected brain metastases (false positives) decreases from 14.2 to 9.12 per patient with the number of subsequently annotated datasets increasing from 93 to 217 as a result of radiologist adjudication.
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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.