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

Area of Science:

• Medical Informatics
• Biomedical Engineering
• Artificial Intelligence in Medicine

Background:

• Artificial intelligence-based decision support systems (AI-DSS) are increasingly utilized in medicine for diagnosis, risk assessment, and treatment planning.
• Vascular surgery, with its reliance on imaging, risk stratification, and complex decisions, presents a significant area for AI-DSS application.

Purpose of the Study:

• To explore the current and potential applications of AI-DSS in vascular surgery.
• To identify the challenges and ethical considerations hindering the adoption of AI-DSS in this field.
• To outline the future directions for integrating AI-DSS into vascular care.

Main Methods:

• Review of deep learning applications in vascular imaging (segmentation, plaque detection, stenosis classification).
• Analysis of intraoperative AI models for endovascular repair (endoleak detection, graft visualization).
• Examination of predictive models for abdominal aortic aneurysm management and postoperative outcomes (complications, survival, reintervention).

Main Results:

• AI-DSS demonstrates high accuracy in image analysis, comparable to expert performance.
• Predictive models enhance risk stratification for aneurysms and forecast postoperative complications.
• Emerging concepts like digital twins and wearable monitoring promise personalized vascular care.

Conclusions:

• AI-DSS holds significant potential to improve precision, safety, and personalization in vascular surgery.
• Barriers to adoption include lack of interpretability, ethical concerns, integration issues, cost, and reimbursement.
• Future progress requires external validation, workflow integration, clinician co-design, and a focus on transparency and trust.