Artificial Intelligence, Machine Learning, and Surgical Science: Reality Versus Hype

被引：14

作者：

El Hechi, Majed ^{[1
]}

Ward, Thomas M. ^{[2
,3
]}

An, Gary C. ^{[4
]}

Maurer, Lydia R. ^{[2
]}

El Moheb, Mohamad ^{[1
]}

Tsoulfas, Georgios ^{[5
]}

Kaafarani, Haytham M. ^{[1
]}

机构：

[1] Massachusetts Gen Hosp, Div Trauma Emergency Surg & Surg Crit Care, 165 Cambridge St,Suite 810, Boston, MA 02114 USA

[2] Massachusetts Gen Hosp, Dept Surg, Boston, MA 02114 USA

[3] Massachusetts Gen Hosp, Surg Artificial Intelligence & Innovat Lab, Boston, MA 02114 USA

[4] Univ Vermont, Dept Surg, Robert Larner MD Coll Med, Div Acute Care Surg, Burlington, VT 05405 USA

[5] Aristotle Univ Thessaloniki, Dept Surg, Thessaloniki, Greece

来源：

JOURNAL OF SURGICAL RESEARCH | 2021年 / 264卷

关键词：

Artificial intelligence; Risk prediction; Machine-learning; Emergency surgery; General surgery; RISK MODEL; CLASSIFICATION; PREDICTION;

D O I：

10.1016/j.jss.2021.01.046

中图分类号：

R61 [外科手术学];

学科分类号：

摘要：

Artificial intelligence (AI) has made increasing inroads in clinical medicine. In surgery, machine learning-based algorithms are being studied for use as decision aids in risk prediction and even for intraoperative applications, including image recognition and video analysis. While AI has great promise in surgery, these algorithms come with a series of potential pitfalls that cannot be ignored as hospital systems and surgeons consider implementing these technologies. The aim of this review is to discuss the progress, promise, and pitfalls of AI in surgery. (C) 2021 Elsevier Inc. All rights reserved.

引用

页码：A1 / A9

页数：9

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