ReviewJournal of personalized medicine2021
Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management.
Review in Journal of personalized medicine, 2021. 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, 21 citations in OpenAlex.
- A deep learning framework for recognizing skin changes secondary to chronic venous insufficiency in clinical photographs: a multicentre validation study.Annals of medicine · 2026Article
- Global, multilingual, physician-invited, patient-completed digital registry enabling pre-consultation engagement and supporting more efficient care pathways in venous disease.Scientific reports · 2026Observational
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- Integrating emerging technologies for varicose veins: A systematic review of comparative efficacy.Pakistan journal of medical sciences · 2026Review
- Machine learning-based prediction models to assess 30-day outcomes following intervention for superficial venous incompetence of the lower limbs.Journal of vascular surgery cases and innovative techniques · 2026Article
- Deep learning algorithm enables lower limb venous thrombosis detection with CT venography.Frontiers in cardiovascular medicine · 2026Article
- Clinical utility of artificial intelligence models in radiology: a systemic scoping review of diagnostic and endovascular applications.CVIR endovascular · 2025Review
- Does Artificial Intelligence Bring New Insights in Diagnosing Phlebological Diseases?-A Systematic Review.Biomedicines · 2025Review
- Molecular signatures of varicosity: Diagnostic insights from ten biomarkers.Caspian journal of internal medicine · 2025Article
- Innovations in MRI and AI Integration for Vascular Plaque Evaluation and Overview of Deep Learning Techniques in Peripheral Vascular Disease.Methodist DeBakey cardiovascular journal · 2025Review
- Radiotherapy for recurrent venous malformation involving the thigh and pelvic organs while preserving organ function: a case report.Frontiers in cardiovascular medicine · 2025Article
- Humans use tools: From handcrafted tools to artificial intelligence.Journal of vascular surgery. Venous and lymphatic disorders · 2024Article
- Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence.Seminars in vascular surgery · 2023Review
- Diffusion-Weighted Imaging Prior to Percutaneous Sclerotherapy of Venous Malformations-Proof of Concept Study for Prediction of Clinical Outcome.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but also in rurality conditions for which access to health centers is constantly limited. The term "AI" indicates the use of a computer to simulate human intellectual behavior with minimal human intervention. AI is based on a "machine learning" process or on an artificial neural network. AI provides accurate diagnostic algorithms and personalized treatments in many fields, including oncology, ophthalmology, traumatology, and dermatology. AI can help vascular specialists in diagnostics of peripheral artery disease, cerebrovascular disease, and deep vein thrombosis by analyzing contrast-enhanced magnetic resonance imaging or ultrasound data and in diagnostics of pulmonary embolism on multi-slice computed angiograms. Automatic methods based on AI may be applied to detect the presence and determine the clinical class of chronic venous disease. Nevertheless, data on using AI in this field are still scarce. In this narrative review, the authors discuss available data on AI implementation in arterial and venous disease diagnostics and care.
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.