Artificial intelligence and machine learning in prediction of surgical complications: Current state, applications, and implications Review
| Authors: | Hassan, A. M.; Rajesh, A.; Asaad, M.; Nelson, J. A.; Coert, J. H.; Mehrara, B. J.; Butler, C. E. |
| Review Title: | Artificial intelligence and machine learning in prediction of surgical complications: Current state, applications, and implications |
| Abstract: | Surgical complications pose significant challenges for surgeons, patients, and health care systems as they may result in patient distress, suboptimal outcomes, and higher health care costs. Artificial intelligence (AI)-driven models have revolutionized the field of surgery by accurately identifying patients at high risk of developing surgical complications and by overcoming several limitations associated with traditional statistics-based risk calculators. This article aims to provide an overview of AI in predicting surgical complications using common machine learning and deep learning algorithms and illustrates how this can be utilized to risk stratify patients preoperatively. This can form the basis for discussions on informed consent based on individualized patient factors in the future. © The Author(s) 2022. |
| Keywords: | algorithms; risk assessment; algorithm; artificial intelligence; surgeon; health care delivery; delivery of health care; surgeons; calculator; machine learning; surgical complications; humans; human; deep learning |
| Journal Title: | American Surgeon |
| Volume: | 89 |
| Issue: | 1 |
| ISSN: | 0003-1348 |
| Publisher: | Southeastern Surgical Congress |
| Date Published: | 2023-01-01 |
| Start Page: | 25 |
| End Page: | 30 |
| Language: | English |
| DOI: | 10.1177/00031348221101488 |
| PUBMED: | 35562124 |
| PROVIDER: | scopus |
| PMCID: | PMC9653510 |
| 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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