Artificial intelligence-based decision support systems and their role in vascular surgery and clinical practice

Artificial intelligence-based decision support systems (AI-DSS) are gaining attention in medicine as tools for diagnosis, risk assessment, and treatment planning. By processing large, heterogeneous datasets, these systems can recognize patterns and generate predictions that complement clinical expertise. Vascular surgery is a relevant case, given its reliance on imaging, patient-specific risk stratification, and complex perioperative choices. Applications in vascular surgery span imaging, intraoperative guidance, and predictive modeling. Deep learning methods support segmentation of aneurysms and peripheral arteries, detection of plaques, and classification of stenoses, achieving accuracy comparable to expert analysis. Intraoperative models aid endoleak detection and graft visualization during endovascular repair. Predictive approaches refine management of abdominal aortic aneurysms by combining imaging, biomechanical modeling, and biomarkers to improve on diameter-based thresholds. Postoperative models forecast complications, survival, and reintervention, while emerging concepts such as digital twins and wearable monitoring suggest more continuous, personalized vascular care. Despite promise, adoption faces barriers. Many models remain "black boxes," raising concerns about interpretability, generalizability, and integration with electronic records. Ethical and legal issues - including accountability, fairness, informed consent, and patient autonomy - are central, with frameworks such as the EU AI Act and FDA guidance beginning to set standards. High development costs and uncertain reimbursement further limit implementation. Building trust will require transparency, reliability, and assurance that AI supports rather than replaces human judgment. Future progress depends on rigorous external validation, workflow integration, and co-design with clinicians. Meeting these conditions could allow AI-DSS to evolve into reliable tools that enhance precision, safety, and personalization in vascular surgery while preserving human values in care.

Authors

Institutions

Publication Details

Journal
The Journal of Cardiovascular Surgery
Published
2025-11-24
DOI
https://doi.org/10.23736/s0021-9509.25.13491-5
Citations
1
Primary Topic
Aortic aneurysm repair treatments
Type
article
Field-Weighted Citation Impact
1.52
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial intelligence-based decision support systems and their role in vascular surgery and clinical practice

Jelmer M. Wolterink, Kevin M. Veen, Noeska Smit, M.C. Ploem et al.
1 citations
The Journal of Cardiovascular Surgery
Aortic aneurysm repair treatments
1.52
article

Artificial intelligence-based decision support systems and their role in vascular surgery and clinical practice

Jelmer M. Wolterink, Kevin M. Veen, Noeska Smit, M.C. Ploem, Kak Khee Yeung, Venkat Ayyalasomayajula, Roosmarie Jessen
article en
1 citations

Abstract

Artificial intelligence-based decision support systems (AI-DSS) are gaining attention in medicine as tools for diagnosis, risk assessment, and treatment planning. By processing large, heterogeneous datasets, these systems can recognize patterns and generate predictions that complement clinical expertise. Vascular surgery is a relevant case, given its reliance on imaging, patient-specific risk stratification, and complex perioperative choices. Applications in vascular surgery span imaging, intraoperative guidance, and predictive modeling. Deep learning methods support segmentation of aneurysms and peripheral arteries, detection of plaques, and classification of stenoses, achieving accuracy comparable to expert analysis. Intraoperative models aid endoleak detection and graft visualization during endovascular repair. Predictive approaches refine management of abdominal aortic aneurysms by combining imaging, biomechanical modeling, and biomarkers to improve on diameter-based thresholds. Postoperative models forecast complications, survival, and reintervention, while emerging concepts such as digital twins and wearable monitoring suggest more continuous, personalized vascular care. Despite promise, adoption faces barriers. Many models remain "black boxes," raising concerns about interpretability, generalizability, and integration with electronic records. Ethical and legal issues - including accountability, fairness, informed consent, and patient autonomy - are central, with frameworks such as the EU AI Act and FDA guidance beginning to set standards. High development costs and uncertain reimbursement further limit implementation. Building trust will require transparency, reliability, and assurance that AI supports rather than replaces human judgment. Future progress depends on rigorous external validation, workflow integration, and co-design with clinicians. Meeting these conditions could allow AI-DSS to evolve into reliable tools that enhance precision, safety, and personalization in vascular surgery while preserving human values in care.

The Journal of Cardiovascular SurgeryVol. 66(6)
Haukeland University Hospital (NO), Erasmus MC (NL), Amsterdam University Medical Centers (NL), University of Bergen (NO), University of Twente (NL)
Openalex Percentile: Top 13%
Aortic aneurysm repair treatments
1.52
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.