ReviewRadiology2024
Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.
Review in Radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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
20 citing papers in PubMed.
- Framework for artificial intelligence implementation research in healthcare: synthesizing current evidence on barriers and facilitators.NPJ digital medicine · 2026Article
- Governance for safe and responsible AI in healthcare organisations: a scoping review of frameworks.NPJ digital medicine · 2026Article
- Hepatic and abdominal adiposity in type 2 diabetes as assessed with machine learning on computed tomography scans.Diabetes, obesity & metabolism · 2026Article
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Hybrid Convolutional Vision Transformer for Robust Low-Channel sEMG Hand Gesture Recognition: A Comparative Study with CNNs.Biomimetics (Basel, Switzerland) · 2025Article
- An Overview of Artificial Intelligence Applications in Radiological Imaging for Bone Fracture Diagnosis.Cureus · 2025Review
- An easily machine learning-based tool for preliminary risk assessment of microvascular invasion in hepatocellular carcinoma.Surgical endoscopy · 2025Article
- Leveraging Datathons to Teach AI in Undergraduate Medical Education: Case Study.JMIR medical education · 2025Article
- Convergent Mechanisms in Virus-Induced Cancers: A Perspective on Classical Viruses, SARS-CoV-2, and AI-Driven Solutions.Infectious disease reports · 2025Article
- A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data.Nature communications · 2025Article
- Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review.Tuberculosis and respiratory diseases · 2025Article
- A Cloud-Based System for Automated AI Image Analysis and Reporting.Journal of imaging informatics in medicine · 2025Article
- HFSA: hybrid feature selection approach to improve medical diagnostic system.PeerJ. Computer science · 2025Article
- Artificial intelligence-based automated breast ultrasound radiomics for breast tumor diagnosis and treatment: a narrative review.Frontiers in oncology · 2025Review
- Effective Structured Information Extraction from Chest Radiography Reports Using Open-Weights Large Language Models.Radiology · 2025Article
- The Future of Academic Neuroradiology: Challenges, Opportunities, and Way Forward.AJNR. American journal of neuroradiology · 2024Article
- Utilizing a domain-specific large language model for LI-RADS v2018 categorization of free-text MRI reports: a feasibility study.Insights into imaging · 2024Article
- Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology.La Radiologia medica · 2024Review
- Article
- Towards equitable AI in oncology.Nature reviews. Clinical oncology · 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
14 authors.
Funding
Abstract
Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a standardized framework for the step-by-step implementation of artificial intelligence into the clinical practice of radiology that focuses on three key components: problem identification, stakeholder alignment, and pipeline integration. A review of the recent literature and empirical evidence in radiologic imaging applications justifies this approach and offers a discussion on structuring implementation efforts to help other hospital practices leverage ML to improve patient care. Clinical trial registration no. 04242667 © RSNA, 2024
Indexed as
Identifiers
What Socratic 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 Socratic graph.