ReviewThe British journal of radiology2023
Clinical applications of artificial intelligence in radiology.
Review in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 4 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
51 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence for predicting the pubertal growth spurt using cephalometric and hand-wrist radiographs: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- Factors influencing AI acceptance in radiology: a systematic review across the radiology workflow.European radiology · 2026Pooled it
- Pooled it
- Artificial intelligence in personalized computed tomography dose optimization: a systematic review of techniques and clinical outcomes.Frontiers in medicine · 2026Pooled it
- Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI.Radiology · 2024Trial
- Greening computed tomography: a position paper on environmentally responsible imaging practice from the computed tomography subspecialty section of the Italian society of radiology.La Radiologia medica · 2026Article
- The current state of demographic subgroup reporting for commercially available AI for radiology: a scoping review.European radiology · 2026Article
- From Embeddings to Accuracy: Comparing Foundation Models for Radiographic Classification.Journal of imaging informatics in medicine · 2026Article
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Review
- Article
- Article
- From promise to practice: a scoping review of AI applications in abdominal radiology.Abdominal radiology (New York) · 2026Article
- State and Diffusion of National Institutes of Health Funding of AI in Radiology.Journal of imaging informatics in medicine · 2026Article
- Knowledge, Utilisation, and Challenges of Medical Doctors Using Picture Archiving and Communication Systems at a Tertiary Academic Hospital in the Eastern Cape, South Africa.Journal of imaging informatics in medicine · 2026Article
- Diagnostic value of artificial intelligence-based software for the detection of pediatric upper extremity fractures.European radiology · 2026Article
- Advances in Artificial Intelligence for Wrist Joint Injury Diagnosis.International journal of medical sciences · 2026Review
- Artificial Intelligence integration and health system performance: effects on diagnostic accuracy, operational efficiency, and workforce outcomes in medical imaging departments.Frontiers in public health · 2026Article
- Development of a deep learning model for intussusception using point-of-care ultrasound.Frontiers in radiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI's adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists' liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.
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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.