H. Taher, Vincent Grasso, Sherifa Tawfik, ANDREW GUMBS
{"title":"The challenges of deep learning in artificial intelligence and autonomous actions in surgery: a literature review","authors":"H. Taher, Vincent Grasso, Sherifa Tawfik, ANDREW GUMBS","doi":"10.20517/ais.2022.11","DOIUrl":null,"url":null,"abstract":"Aim: Artificial intelligence (AI) is rapidly evolving in healthcare worldwide, especially in surgery. This article reviews important terms used in machine learning and the challenges of deep learning in surgery. Methods: A review of the English literature was carried out focused on the terms “challenges of deep learning” and “surgery” using Medline and PubMed between 2018 and 2022. Results: In total, 54 articles discussed the challenges of deep learning in general. We include 25 articles from various surgical specialties discussing challenges corresponding to their respective specialties. Conclusion: The increased utilization of AI in surgery is faced with a wide variety of technical, ethical, clinical, and business-related challenges. The best way to expedite its expansion in surgery in the safest and most cost-efficient manner is by ensuring that as many surgeons as possible have a clear understanding of basic AI concepts and how they can be applied to the preoperative, intraoperative, postoperative, and long-term follow-up phases of the surgical patient care.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial intelligence surgery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20517/ais.2022.11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 29
Abstract
Aim: Artificial intelligence (AI) is rapidly evolving in healthcare worldwide, especially in surgery. This article reviews important terms used in machine learning and the challenges of deep learning in surgery. Methods: A review of the English literature was carried out focused on the terms “challenges of deep learning” and “surgery” using Medline and PubMed between 2018 and 2022. Results: In total, 54 articles discussed the challenges of deep learning in general. We include 25 articles from various surgical specialties discussing challenges corresponding to their respective specialties. Conclusion: The increased utilization of AI in surgery is faced with a wide variety of technical, ethical, clinical, and business-related challenges. The best way to expedite its expansion in surgery in the safest and most cost-efficient manner is by ensuring that as many surgeons as possible have a clear understanding of basic AI concepts and how they can be applied to the preoperative, intraoperative, postoperative, and long-term follow-up phases of the surgical patient care.