A Surgeon’s Guide to Artificial Intelligence-Driven Predictive Models

Author:

Hassan Abbas M.1,Rajesh Aashish2,Asaad Malke3,Nelson Jonas A.4,Coert J. Henk5,Mehrara Babak J.4,Butler Charles E.1

Affiliation:

1. Department of Plastic & Reconstructive Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

2. Department of Surgery, University of Texas Health Science Center, San Antonio, TX, USA

3. Department of Plastic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA

4. Department of Plastic & Reconstructive Surgery, Memorial Sloan Kettering Cancer Center, New York, NY

5. Department of Plastic and Reconstructive Surgery, University Medical Center Utrecht, Utrecht, Netherlands

Abstract

Artificial intelligence (AI) focuses on processing and interpreting complex information as well as identifying relationships and patterns among complex data. Artificial intelligence- and machine learning (ML)-driven predictions have shown promising potential in influencing real-time decisions and improving surgical outcomes by facilitating screening, diagnosis, risk assessment, preoperative planning, and shared decision-making. Fundamental understanding of the algorithms, as well as their development and interpretation, is essential for the evolution of AI in surgery. In this article, we provide surgeons with a fundamental understanding of AI-driven predictive models through an overview of common ML and deep learning algorithms, model development, performance metrics and interpretation. This would serve as a basis for understanding ML-based research, while fostering new ideas and innovations for furthering the reach of this emerging discipline.

Publisher

SAGE Publications

Subject

General Medicine

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