Comprehensive review of virtual assistants in vascular surgery.


Journal

Seminars in vascular surgery
ISSN: 1558-4518
Titre abrégé: Semin Vasc Surg
Pays: United States
ID NLM: 8809602

Informations de publication

Date de publication:
Sep 2024
Historique:
received: 30 01 2024
revised: 15 06 2024
accepted: 02 07 2024
medline: 15 9 2024
pubmed: 15 9 2024
entrez: 14 9 2024
Statut: ppublish

Résumé

Virtual assistants, broadly defined as digital services designed to simulate human conversation and provide personalized responses based on user input, have the potential to improve health care by supporting clinicians and patients in terms of diagnosing and managing disease, performing administrative tasks, and supporting medical research and education. These tasks are particularly helpful in vascular surgery, where the clinical and administrative burden is high due to the rising incidence of vascular disease, the medical complexity of the patients, and the potential for innovation and care advancement. The rapid development of artificial intelligence, machine learning, and natural language processing techniques have facilitated the training of large language models, such as GPT-4 (OpenAI), which can support the development of increasingly powerful virtual assistants. These tools may support holistic, multidisciplinary, and high-quality vascular care delivery throughout the pre-, intra-, and postoperative stages. Importantly, it is critical to consider the design, safety, and challenges related to virtual assistants, including data security, ethical, and equity concerns. By combining the perspectives of patients, clinicians, data scientists, and other stakeholders when developing, implementing, and monitoring virtual assistants, there is potential to harness the power of this technology to care for vascular surgery patients more effectively. In this comprehensive review article, we introduce the concept of virtual assistants, describe potential applications of virtual assistants in vascular surgery for clinicians and patients, highlight the benefits and drawbacks of large language models, such as GPT-4, and discuss considerations around the design, safety, and challenges associated with virtual assistants in vascular surgery.

Identifiants

pubmed: 39277351
pii: S0895-7967(24)00036-X
doi: 10.1053/j.semvascsurg.2024.07.001
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

342-349

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Auteurs

Ben Li (B)

Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Suite 7-074, Bond Wing, Toronto, ON, Canada, M5B 1W8; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada.

Derek Beaton (D)

Data Science and Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada.

Douglas S Lee (DS)

Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada.

Badr Aljabri (B)

Department of Surgery, King Saud University, Saudi Arabia.

Leen Al-Omran (L)

School of Medicine, Alfaisal University, Saudi Arabia.

Duminda N Wijeysundera (DN)

Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada; Department of Anesthesia, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.

Mohamad A Hussain (MA)

Division of Vascular and Endovascular Surgery and the Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.

Ori D Rotstein (OD)

Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Division of General Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.

Charles de Mestral (C)

Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Suite 7-074, Bond Wing, Toronto, ON, Canada, M5B 1W8; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.

Muhammad Mamdani (M)

Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada; Data Science and Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada.

Mohammed Al-Omran (M)

Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Suite 7-074, Bond Wing, Toronto, ON, Canada, M5B 1W8; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Saudi Arabia. Electronic address: mohammed.al-omran@unityhealth.to.

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