ReviewDiagnostics (Basel, Switzerland)2025
AI and Interventional Radiology: A Narrative Review of Reviews on Opportunities, Challenges, and Future Directions.
Review in Diagnostics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Clinical Implementation and Performance of Real-Time AI System for Device Tracking in Neurointervention: First U.S. Evaluation of Neuro-Vascular Assist.Clinical neuroradiology · 2026Article
- Integrating artificial intelligence into paediatric interventional radiology: a review of emerging applications and future directions.Pediatric radiology · 2026Review
- Point-of-care ultrasound (POCUS) in emergency airway management: emerging evidence for assessment, prediction, and confirmation.Internal and emergency medicine · 2026Review
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- A Pilot Study Protocol for AI-Assisted Interpretation of Chest X-rays for Pulmonary Abnormalities in Uganda.Cureus · 2026Article
- An Explainable Transformer-Based Framework for Lung Cancer Classification and Automated Radiology Report Generation from Multi-Slice CT Images.Biomedicines · 2026Article
- Ultrasound-guided ablation of hepatocellular carcinoma: a review of its past, present, and future.Ultrasonography (Seoul, Korea) · 2026Article
- Early clinical experiences with AI-based EVAR planning using the Endoleak Risk Index support its value for individualized decision-making and education.Journal of vascular surgery cases and innovative techniques · 2026Article
- AI in radiology and interventions: a structured narrative review of workflow automation, accuracy, and efficiency gains of today and what's coming.International journal of computer assisted radiology and surgery · 2026Review
- Physicians' perceptions of how digital-intelligent medical technology reshapes job characteristics: a qualitative study.BMC health services research · 2025Article
- Advancements and challenges in autonomous endovascular interventional robotics: A comprehensive review.iScience · 2025Review
- Artificial Intelligence in Thermal Ablation: Current Applications and Future Directions in Microwave Technologies.Biomimetics (Basel, Switzerland) · 2025Review
- Chatbots in Radiology: Current Applications, Limitations and Future Directions of ChatGPT in Medical Imaging.Diagnostics (Basel, Switzerland) · 2025Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence in interventional radiology is an emerging field with transformative potential, aiming to make a great contribution to the health domain. This overview of reviews seeks to identify prevailing themes, opportunities, challenges, and recommendations related to the process of integration. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements in, and future implications of, this domain. In total, 27 review studies were selected through the systematic process. Based on the overview, the integration of artificial intelligence (AI) in interventional radiology (IR) presents significant opportunities to enhance precision, efficiency, and personalization of procedures. AI automates tasks like catheter manipulation and needle placement, improving accuracy and reducing variability. It also integrates multiple imaging modalities, optimizing treatment planning and outcomes. AI aids intra-procedural guidance with advanced needle tracking and real-time image fusion. Robotics and automation in IR are advancing, though full autonomy in AI-guided systems has not been achieved. Despite these advancements, the integration of AI in IR is complex, involving imaging systems, robotics, and other technologies. This complexity requires a comprehensive certification and integration process. The role of regulatory bodies, scientific societies, and clinicians is essential to address these challenges. Standardized guidelines, clinician education, and careful AI assessment are necessary for safe integration. The future of AI in IR depends on developing standardized guidelines for medical devices and AI applications. Collaboration between certifying bodies, scientific societies, and legislative entities, as seen in the EU AI Act, will be crucial to tackling AI-specific challenges. Focusing on transparency, data governance, human oversight, and post-market monitoring will ensure AI integration in IR proceeds with safeguards, benefiting patient outcomes and advancing the field.
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.