A surgeon’s guide to artificial intelligence-driven predictive models Review


Authors: Hassan, A. M.; Rajesh, A.; Asaad, M.; Nelson, J. A.; Coert, J. H.; Mehrara, B. J.; Butler, C. E.
Review Title: A surgeon’s guide to artificial intelligence-driven predictive models
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. © The Author(s) 2022.
Keywords: outcome assessment; preoperative evaluation; algorithms; prediction; risk assessment; algorithm; artificial intelligence; surgeon; surgery; shared decision making; surgeons; machine learning; predictive model; humans; human; article; deep learning; performance indicator
Journal Title: American Surgeon
Volume: 89
Issue: 1
ISSN: 0003-1348
Publisher: Southeastern Surgical Congress  
Date Published: 2023-01-01
Start Page: 11
End Page: 19
Language: English
DOI: 10.1177/00031348221103648
PUBMED: 35588764
PROVIDER: scopus
PMCID: PMC9674797
DOI/URL:
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PubMed record. -- Export Date: 3 January 2023 -- Source: Scopus
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  1. Babak Mehrara
    451 Mehrara
  2. Jonas Allan Nelson
    212 Nelson