ORIGINAL RESEARCH article
Front. Surg.
Sec. Vascular Surgery
This article is part of the Research TopicThe Use of Artificial Intelligence for Diagnostics and Treatment in Vascular SurgeryView all 3 articles
Predictive Modelling of Vascular Surgery Trends Using Machine Learning: A Comparative Study of Irish Public and Private Tertiary Referral Centres
Provisionally accepted
- 1Western Vascular Institute,Department of Vascular and Endovascular Surgery, University Hospital Galway, University of Galway, Galway, Ireland
- 2Galway Clinic, Galway, Ireland
- 3Royal College of Surgeons in Ireland, Dublin, Ireland
- 4Letterkenny University Hospital, Letterkenny, Ireland
- 5Mater Private Network, Cardiovascular Research Institute Dublin, Dublin, Ireland
- 6Euro Heart Foundation, Amsterdam, Netherlands
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: Vascular diseases are increasing in Ireland as well as worldwide alongside an ageing society, posing a growing demand for trained and qualified healthcare professionals. In this study, we have analysed current practices of vascular interventions by using the data from the vascular tertiary centre to predict the future size and capacity of the vascular surgery workforce through artificial intelligence (AI)-powered predictive models. Methods: We employed supervised machine learning (ML) regression model to predict trends in the landscape of complex vascular and endovascular surgery over the next 22 years, utilising data from a high-volume public and private tertiary referral vascular centre spanning two decades (2002 to 2023) in the West of Ireland. Results: We conducted 1,653 aortic interventions, 1,185 carotid interventions, and 3,069 peripheral vascular interventions, with conversion rates from referral to surgery of 5%, 7.4%, and 9.8%, respectively. The private sector experienced a dramatic 73-fold increase in abdominal aortic aneurysm interventions, contrasted with a modest 1.25-fold increase in the public sector. Our model predicts a shortage of vascular surgeons, with the workforce potentially meeting demand by 2050. By 2030, each surgeon would need to increase yearly wRVU production by 22-31% and by 2040 by 8-11% to accommodate the workload. Conclusions: Our model predicts a shortage of the vascular surgery workforce over the next two decades. We can speculate that addressing future needs in vascular surgery requires either training more specialists or increasing the efficiency and wRUV through strategic planning and integration of AI/ML systems to ensure adequate compensation and the sustainability of the workforce. By focusing on these areas, we can navigate the evolving landscape of vascular surgery and continue providing high-quality patient care.
Keywords: Vascular Surgery, predictive models, machine learning, Public-Private Hospitals, trend analysis
Received: 27 Oct 2025; Accepted: 12 Dec 2025.
Copyright: © 2025 Sultan, Acharya, Sultan and SOLIMAN. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sherif Sultan
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.