ReviewJournal of breast imaging
Artificial Intelligence in Clinical Practice: Implementation Considerations and Barriers.
Review in Journal of breast imaging. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Integration of AI-driven ultrasound and molecular testing to improve diagnosis of indeterminate thyroid nodules.Frontiers in endocrinology · 2026Pooled it
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- Breast Remote Reading: Widely Desired But Home Workstations Show No Association With Job Satisfaction Or Burnout.Journal of breast imaging · 2025Article
- The impact of leadership on AI deployment study outcomes in healthcare: an integrative analysis.NPJ digital medicine · 2025Article
- Artificial Intelligence (AI)-Based Computer-Assisted Detection and Diagnosis for Mammography: An Evidence-Based Review of Food and Drug Administration (FDA)-Cleared Tools for Screening Digital Breast Tomosynthesis (DBT).AI in precision oncology · 2024Review
- New Frontiers in Breast Cancer Imaging: The Rise of AI.Bioengineering (Basel, Switzerland) · 2024Review
- Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway.Radiology. Artificial intelligence · 2024Article
- Implementing AI in breast imaging: challenges to turn the gadget into gain.European radiology · 2024Article
- Clinical applications of artificial intelligence in radiology.The British journal of radiology · 2023Review
- Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions.Diagnostics (Basel, Switzerland) · 2023Review
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 at 1 institution in 1 country.
Funding
Abstract
The rapid growth of artificial intelligence (AI) in radiology has led to Food and Drug Administration clearance of more than 20 AI algorithms for breast imaging. The steps involved in the clinical implementation of an AI product include identifying all stakeholders, selecting the appropriate product to purchase, evaluating it with a local data set, integrating it into the workflow, and monitoring its performance over time. Despite the potential benefits of improved quality and increased efficiency with AI, several barriers, such as high costs and liability concerns, may limit its widespread implementation. This article lists currently available AI products for breast imaging, describes the key elements of clinical implementation, and discusses barriers to clinical implementation.
Indexed as
Identifiers
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.