ReviewFrontiers in molecular biosciences2020
Artificial Intelligence for the Future Radiology Diagnostic Service.
Review in Frontiers in molecular biosciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 3 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
42 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Clinical impact of AI in radiology department management: a systematic review.La Radiologia medica · 2024Pooled it
- Machine Learning in Medical Emergencies: a Systematic Review and Analysis.Journal of medical systems · 2021Pooled it
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
- AI Software Among Commercially Insured Populations: Cross-Sectional Study of Patient and Plan Characteristics.JMIR formative research · 2026Article
- Global physician perspectives on artificial intelligence in healthcare across 50 countries and territories.NPJ digital medicine · 2026Article
- Advice quality and source disclosure shape trust in AI-generated ethical advice.Scientific reports · 2026Article
- Exploring AI in radiology: a bibliometric study tailored to a research path.Radiologie (Heidelberg, Germany) · 2026Article
- Article
- Urological diagnostics based on kidney stone detection in CT imaging using YOLOv8 deep learning framework.Frontiers in medicine · 2026Article
- Large language models and large concept models in radiology: Present challenges, future directions, and critical perspectives.World journal of radiology · 2025Review
- Review
- Artificial intelligence in pancreatic cancer histopathology and diagnostics - implications for clinical decisions and biomarker discovery?Cell division · 2025Review
- A multi-site study of clinician perspectives in the lifecycle of an algorithmic risk prediction tool.SSM. Qualitative research in health · 2025Article
- Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.Diagnostics (Basel, Switzerland) · 2025Review
- Galar - a large multi-label video capsule endoscopy dataset.Scientific data · 2025Article
- The unquestionable marriage between AI and structured reporting.European radiology · 2025Article
- AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- Patient Consent and The Right to Notice and Explanation of AI Systems Used in Health Care.The American journal of bioethics : AJOB · 2025Article
- The role of AI in mitigating the impact of radiologist shortages: a systematised review.Health and technology · 2025Review
- Use of artificial intelligence in the management of stroke: scoping review.Frontiers in radiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.
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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.