ArticleJournal of the American College of Radiology : JACR2019
Artificial Intelligence in Imaging: The Radiologist's Role.
Article in Journal of the American College of Radiology : JACR, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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
46 citing papers in PubMed.
- Patient Reactions to Artificial Intelligence-Clinician Discrepancies: Web-Based Randomized Experiment.Journal of medical Internet research · 2025Trial
- Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies.PloS one · 2021Trial
- Triage and workflow optimization with artificial intelligence in pediatric imaging.Pediatric radiology · 2026Review
- Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology.Radiology. Artificial intelligence · 2026Article
- From Artificial Intelligence to Robotics: How to Navigate Technological Innovation in Radiology With the Gartner Hype Cycle.Academic radiology · 2026Review
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Article
- Leadership in radiology in the era of technological advancements and artificial intelligence.European radiology · 2026Article
- Large language models and large concept models in radiology: Present challenges, future directions, and critical perspectives.World journal of radiology · 2025Review
- Reimagining healthcare education through nurturing AI-driven innovation.BMC medical education · 2025Review
- Artificial intelligence in dentistry: awareness among dentists and computer scientists.Oral radiology · 2025Article
- It is Not Time to Kick Out Radiologists.Asian bioethics review · 2025Article
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- Commentary: Comparison of radiological interpretation made by veterinary radiologists and state-of-the-art commercial AI software for canine and feline radiographic studies.Frontiers in veterinary science · 2025Article
- Navigating the Spectrum: Assessing the Concordance of ML-Based AI Findings with Radiology in Chest X-Rays in Clinical Settings.Healthcare (Basel, Switzerland) · 2024Article
- Article
- Five Things That Radiologists Can Do to Improve Their Technology Quotient.The Indian journal of radiology & imaging · 2024Article
- Expert gaze as a usability indicator of medical AI decision support systems: a preliminary study.NPJ digital medicine · 2024Article
- Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity.Scientific reports · 2024Article
- Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Rapid technological advancements in artificial intelligence (AI) methods have fueled explosive growth in decision tools being marketed by a rapidly growing number of companies. AI developments are being driven largely by computer scientists, informaticians, engineers, and businesspeople, with much less direct participation by radiologists. Participation by radiologists in AI is largely restricted to educational efforts to familiarize them with the tools and promising results, but techniques to help them decide which AI tools should be used in their practices and to how to quantify their value are not being addressed. This article focuses on the role of radiologists in imaging AI and suggests specific ways they can be engaged by (1) considering the clinical need for AI tools in specific clinical use cases, (2) undertaking formal evaluation of AI tools they are considering adopting in their practices, and (3) maintaining their expertise and guarding against the pitfalls of overreliance on technology.
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What OpenQuestion holds
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.