H. Taher, Vincent Grasso, Sherifa Tawfik, ANDREW GUMBS
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.
{"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":"https://doi.org/10.20517/ais.2022.11","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":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ANDREW GUMBS, Frank Alexander, K. Karcz, E. Chouillard, R. Croner, J. Coles-Black, B. De Simone, M. Gagner, B. Gayet, Vincent Grasso, A. Illanes, T. Ishizawa, L. Milone, Mehmet Mahir Özmen, M. Piccoli, Stefanie Spiedel, G. Spolverato, P. Sylla, Jaime Vilaça, L. Swanström
This white paper documents the consensus opinion of the expert members of the Editorial Board of Artificial Intelligence Surgery regarding the definitions of artificial intelligence and autonomy in regards to surgery and how the digital evolution of surgery is interrelated with the various forms of robotic-assisted surgery. It was derived from a series of video conference discussions, and the survey and results were subsequently revised and approved by all authors.
{"title":"White paper: definitions of artificial intelligence and autonomous actions in clinical surgery","authors":"ANDREW GUMBS, Frank Alexander, K. Karcz, E. Chouillard, R. Croner, J. Coles-Black, B. De Simone, M. Gagner, B. Gayet, Vincent Grasso, A. Illanes, T. Ishizawa, L. Milone, Mehmet Mahir Özmen, M. Piccoli, Stefanie Spiedel, G. Spolverato, P. Sylla, Jaime Vilaça, L. Swanström","doi":"10.20517/ais.2022.10","DOIUrl":"https://doi.org/10.20517/ais.2022.10","url":null,"abstract":"This white paper documents the consensus opinion of the expert members of the Editorial Board of Artificial Intelligence Surgery regarding the definitions of artificial intelligence and autonomy in regards to surgery and how the digital evolution of surgery is interrelated with the various forms of robotic-assisted surgery. It was derived from a series of video conference discussions, and the survey and results were subsequently revised and approved by all authors.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Isaac Tranter-Entwistle, T. Eglinton, S. Connor, T. Hugh
Aim: Computer vision is a subset of machine learning (ML) technology that allows automated analysis of large operative video datasets. The aim of this study was to use a commercially available ML-driven platform to evaluate a subjective grading of operative difficulty in laparoscopic cholecystectomy (LC). Methods: Patients undergoing LC prospectively consented, and their operations were recorded. The intra-operative findings were prospectively graded (1-4) based on intraoperative gallbladder appearance assessments. Deidentified videos were uploaded to Touch SurgeryTMand run through the platform’s algorithm, providing automated analytics including the total operative length and operative phase length. The rate of critical view of safety (CVS) achievement was also included in the analysis. Results: 206 LC were included. 27 LC were excluded due to incomplete video recording and were therefore not amenable to the final data analysis. Grade 1 and 2 patients had significantly shorter operative time than grade 3 and 4 patients [17min and 53s (IQR 15min and 24s- 21min and 38s) vs. 25 min and 49s (IQR 20min and 12s-38min and 38s) (P < 0.010)]. The operative phases for each step were significantly longer in patients with gallbladders graded 3 or 4 compared to those patients graded 1 or 2 (P < 0.043). The CVS was achieved in 94% of grade 1 patients, 88% of grade 2 patients, 85% of grade 3 patients and 73% of grade 4 patients (P = 0.177). Conclusion: Increased operative time and decreased ability to achieve the CVS with more difficult intraoperative findings supports the utility of the proposed grading system. ML in surgery is a nascent field, but this study demonstrates the potential of commercially available platforms for use in operative analytics, documentation, audit and training of future surgeons.
{"title":"Operative difficulty in laparoscopic cholecystectomy: considering the role of machine learning platforms in clinical practice","authors":"Isaac Tranter-Entwistle, T. Eglinton, S. Connor, T. Hugh","doi":"10.20517/ais.2022.01","DOIUrl":"https://doi.org/10.20517/ais.2022.01","url":null,"abstract":"Aim: Computer vision is a subset of machine learning (ML) technology that allows automated analysis of large operative video datasets. The aim of this study was to use a commercially available ML-driven platform to evaluate a subjective grading of operative difficulty in laparoscopic cholecystectomy (LC). Methods: Patients undergoing LC prospectively consented, and their operations were recorded. The intra-operative findings were prospectively graded (1-4) based on intraoperative gallbladder appearance assessments. Deidentified videos were uploaded to Touch SurgeryTMand run through the platform’s algorithm, providing automated analytics including the total operative length and operative phase length. The rate of critical view of safety (CVS) achievement was also included in the analysis. Results: 206 LC were included. 27 LC were excluded due to incomplete video recording and were therefore not amenable to the final data analysis. Grade 1 and 2 patients had significantly shorter operative time than grade 3 and 4 patients [17min and 53s (IQR 15min and 24s- 21min and 38s) vs. 25 min and 49s (IQR 20min and 12s-38min and 38s) (P < 0.010)]. The operative phases for each step were significantly longer in patients with gallbladders graded 3 or 4 compared to those patients graded 1 or 2 (P < 0.043). The CVS was achieved in 94% of grade 1 patients, 88% of grade 2 patients, 85% of grade 3 patients and 73% of grade 4 patients (P = 0.177). Conclusion: Increased operative time and decreased ability to achieve the CVS with more difficult intraoperative findings supports the utility of the proposed grading system. ML in surgery is a nascent field, but this study demonstrates the potential of commercially available platforms for use in operative analytics, documentation, audit and training of future surgeons.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Runwen Liu, Jingjing An, Ziyao Wang, Jingye Guan, Jie Liu, Jingwen Jiang, Zhimin Chen, Hai Li, B. Peng, Xin Wang
Background: The occurrence of biliary duct injury (BDI) after laparoscopic cholecystectomy (LC) remains 0.2-1.5%, which is largely caused by anatomic misidentifications. To solve this problem, we developed an artificial intelligence model, SurgSmart, and preliminarily verified its potential surgical guidance ability by comparing its performance with surgeons. Methods: We prospectively collected 60 LC videos from November 2019 to August 2020 and enrolled 41 videos into the model establishment. Four important anatomic regions, namely cystic duct, cystic artery, common bile duct, and cystic plate, were annotated, and YOLOv3 (You Look Only Once), an object detection algorithm, was applied to develop the model SurgSmart. To further evaluate its performance, comparisons were made among SurgSmart, trainees, and seniors (surgical experience in LC > 100). Results: In total, 101,863 frames were extracted from videos, and 5533 video frames were selected, annotated, and used in model training. The mean average precision (mAP) of SurgSmart was 0.710. Comparative results show SurgSmart had significantly higher intersection-over-union (IoU) and accuracy (IoU ≥ 0.5) in anatomy detection than those of seniors (n = 36) and trainees (n = 32) despite the existence of severe inflammation. Additionally, SurgSmart tended to correctly identify anatomic regions in earlier surgical phases than most of the seniors and trainees (P < 0.001). Conclusions: SurgSmart is not only capable of accurately detecting and positioning anatomic regions in LC but also has better performance than that of the trainees and seniors in terms of individual still images and the whole set.
{"title":"Artificial intelligence in laparoscopic cholecystectomy: does computer vision outperform human vision?","authors":"Runwen Liu, Jingjing An, Ziyao Wang, Jingye Guan, Jie Liu, Jingwen Jiang, Zhimin Chen, Hai Li, B. Peng, Xin Wang","doi":"10.20517/ais.2022.04","DOIUrl":"https://doi.org/10.20517/ais.2022.04","url":null,"abstract":"Background: The occurrence of biliary duct injury (BDI) after laparoscopic cholecystectomy (LC) remains 0.2-1.5%, which is largely caused by anatomic misidentifications. To solve this problem, we developed an artificial intelligence model, SurgSmart, and preliminarily verified its potential surgical guidance ability by comparing its performance with surgeons. Methods: We prospectively collected 60 LC videos from November 2019 to August 2020 and enrolled 41 videos into the model establishment. Four important anatomic regions, namely cystic duct, cystic artery, common bile duct, and cystic plate, were annotated, and YOLOv3 (You Look Only Once), an object detection algorithm, was applied to develop the model SurgSmart. To further evaluate its performance, comparisons were made among SurgSmart, trainees, and seniors (surgical experience in LC > 100). Results: In total, 101,863 frames were extracted from videos, and 5533 video frames were selected, annotated, and used in model training. The mean average precision (mAP) of SurgSmart was 0.710. Comparative results show SurgSmart had significantly higher intersection-over-union (IoU) and accuracy (IoU ≥ 0.5) in anatomy detection than those of seniors (n = 36) and trainees (n = 32) despite the existence of severe inflammation. Additionally, SurgSmart tended to correctly identify anatomic regions in earlier surgical phases than most of the seniors and trainees (P < 0.001). Conclusions: SurgSmart is not only capable of accurately detecting and positioning anatomic regions in LC but also has better performance than that of the trainees and seniors in terms of individual still images and the whole set.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
and tuberculomas ( P < 0.0001). Other were able to identify radiomic features specific for EGFR mutant vs. wild type groups and K-ras mutations an application designed to stratify lung adenocarcinoma into aggressive and minimally uses nine representative characteristics to identify the histopathology standardized uptake value (SUV), maximum standardized uptake significantly T N stage.
{"title":"The cassandra paradox: looking into the crystal Ball of radiomics in thoracic surgery","authors":"J. Decker, J. Sesti, Amber L. Turner, S. Paul","doi":"10.20517/ais.2022.05","DOIUrl":"https://doi.org/10.20517/ais.2022.05","url":null,"abstract":"and tuberculomas ( P < 0.0001). Other were able to identify radiomic features specific for EGFR mutant vs. wild type groups and K-ras mutations an application designed to stratify lung adenocarcinoma into aggressive and minimally uses nine representative characteristics to identify the histopathology standardized uptake value (SUV), maximum standardized uptake significantly T N stage.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aim of this work is to offer a panoramic view on how artificial intelligence (AI) can help to break down gender disparity in enrollment and training of women in surgery. Nowadays, many visible and concealed obstacles still exist for women who pursue a surgical career. Impediments due to gender disparity prevent women from choosing surgical specialties. Furthermore, female surgical trainees have to face many difficulties during their training, such as inequity during the residency selection process, sexual harassment, discrimination in pregnancy experience and parental leave, and work-life balance problems. AI has been successfully employed for several applications in surgery to improve patient management, implement the decision-making process, and support training. AI could represent an effective way to overcome barriers related to gender disparity and overcome the obstacles women face during surgical education and training. Virtual and augmented reality, remote mentoring, and simulators could help female surgeons deal with disparities during their training and could positively impact the choice of women when pursuing a surgical career.
{"title":"The potential of artificial intelligence as an equalizer of gender disparity in surgical training and education","authors":"V. Mari, G. Spolverato, L. Ferrari","doi":"10.20517/ais.2022.12","DOIUrl":"https://doi.org/10.20517/ais.2022.12","url":null,"abstract":"The aim of this work is to offer a panoramic view on how artificial intelligence (AI) can help to break down gender disparity in enrollment and training of women in surgery. Nowadays, many visible and concealed obstacles still exist for women who pursue a surgical career. Impediments due to gender disparity prevent women from choosing surgical specialties. Furthermore, female surgical trainees have to face many difficulties during their training, such as inequity during the residency selection process, sexual harassment, discrimination in pregnancy experience and parental leave, and work-life balance problems. AI has been successfully employed for several applications in surgery to improve patient management, implement the decision-making process, and support training. AI could represent an effective way to overcome barriers related to gender disparity and overcome the obstacles women face during surgical education and training. Virtual and augmented reality, remote mentoring, and simulators could help female surgeons deal with disparities during their training and could positively impact the choice of women when pursuing a surgical career.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aim: The purpose of this study was to investigate if principles of Artificial Intelligence (AI), specifically Natural Language Processing (NLP), could be applied to the personal statements of general surgery residency applicants in order to gain valuable insight into the candidates and facilitate a more comprehensive assessment. Methods: The personal statements from individuals applying for a general surgery residency position during the 2021/22 application cycle (n = 1792) were analyzed using AI technology. Comparison groups were drawn from a database of documents from the general population and the personal statements of current general surgery residents (n = 64) at a single academic center. The study was conducted in collaboration with a leading language psychology and natural language processing organization. Results: Applicants exhibited a language-based personality that was highly self-assured (P < 0.0001) and trusting (P < 0.0001), and less stress-prone (P < 0.0001) and impulsive (P < 0.0001) than that of the general population. Compared to the general applicant pool, current residents were significantly more emotionally aware (P < 0.001) and organized (P < 0.001) and less self-assured (P < 0.001) and less driven by power (P < 0.001). Conclusion: Natural language processing technology can be utilized to assess the unique characteristics of general surgery resident applicants based on the content of their personal statements. In addition, candidates who successfully gain admission to a single academic program display different language-based personalities and drives compared to the general applicant pool. Incorporating these principles of artificial intelligence into the residency selection process could facilitate a more holistic evaluation of candidates.
{"title":"Leveraging artificial intelligence for resident recruitment: can the dream of holistic review be realized?","authors":"A. S. John, S. Kavic","doi":"10.20517/ais.2022.24","DOIUrl":"https://doi.org/10.20517/ais.2022.24","url":null,"abstract":"Aim: The purpose of this study was to investigate if principles of Artificial Intelligence (AI), specifically Natural Language Processing (NLP), could be applied to the personal statements of general surgery residency applicants in order to gain valuable insight into the candidates and facilitate a more comprehensive assessment. Methods: The personal statements from individuals applying for a general surgery residency position during the 2021/22 application cycle (n = 1792) were analyzed using AI technology. Comparison groups were drawn from a database of documents from the general population and the personal statements of current general surgery residents (n = 64) at a single academic center. The study was conducted in collaboration with a leading language psychology and natural language processing organization. Results: Applicants exhibited a language-based personality that was highly self-assured (P < 0.0001) and trusting (P < 0.0001), and less stress-prone (P < 0.0001) and impulsive (P < 0.0001) than that of the general population. Compared to the general applicant pool, current residents were significantly more emotionally aware (P < 0.001) and organized (P < 0.001) and less self-assured (P < 0.001) and less driven by power (P < 0.001). Conclusion: Natural language processing technology can be utilized to assess the unique characteristics of general surgery resident applicants based on the content of their personal statements. In addition, candidates who successfully gain admission to a single academic program display different language-based personalities and drives compared to the general applicant pool. Incorporating these principles of artificial intelligence into the residency selection process could facilitate a more holistic evaluation of candidates.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Wagner, S. Bodenstedt, M. Daum, A. Schulze, Rayan Younis, Johanna M. Brandenburg, F. Kolbinger, M. Distler, L. Maier-Hein, J. Weitz, B. Müller-Stich, S. Speidel
Surgery faces a paradigm shift since it has developed rapidly in recent decades, becoming a high-tech discipline. Increasingly powerful technological developments such as modern operating rooms, featuring digital and interconnected equipment and novel imaging as well as robotic procedures, provide several data sources resulting in a huge potential to improve patient therapy and surgical outcome by means of Surgical Data Science. The emerging field of Surgical Data Science aims to improve the quality of surgery through acquisition, organization, analysis, and modeling of data, in particular using machine learning (ML). An integral part of surgical data science is to analyze the available data along the surgical treatment path and provide a context-aware autonomous action by means of ML methods. Autonomous actions related to surgical decision-making include preoperative decision support, intraoperative assistance functions, as well as robot-assisted actions. The goal is to democratize surgical skills and enhance the collaboration between surgeons and cyber-physical systems by quantifying surgical experience and making it accessible to machines, thereby improving patient therapy and outcome. The article introduces basic ML concepts as enablers for autonomous actions in surgery, highlighting examples for such actions along the surgical treatment path.
{"title":"The importance of machine learning in autonomous actions for surgical decision making","authors":"M. Wagner, S. Bodenstedt, M. Daum, A. Schulze, Rayan Younis, Johanna M. Brandenburg, F. Kolbinger, M. Distler, L. Maier-Hein, J. Weitz, B. Müller-Stich, S. Speidel","doi":"10.20517/ais.2022.02","DOIUrl":"https://doi.org/10.20517/ais.2022.02","url":null,"abstract":"Surgery faces a paradigm shift since it has developed rapidly in recent decades, becoming a high-tech discipline. Increasingly powerful technological developments such as modern operating rooms, featuring digital and interconnected equipment and novel imaging as well as robotic procedures, provide several data sources resulting in a huge potential to improve patient therapy and surgical outcome by means of Surgical Data Science. The emerging field of Surgical Data Science aims to improve the quality of surgery through acquisition, organization, analysis, and modeling of data, in particular using machine learning (ML). An integral part of surgical data science is to analyze the available data along the surgical treatment path and provide a context-aware autonomous action by means of ML methods. Autonomous actions related to surgical decision-making include preoperative decision support, intraoperative assistance functions, as well as robot-assisted actions. The goal is to democratize surgical skills and enhance the collaboration between surgeons and cyber-physical systems by quantifying surgical experience and making it accessible to machines, thereby improving patient therapy and outcome. The article introduces basic ML concepts as enablers for autonomous actions in surgery, highlighting examples for such actions along the surgical treatment path.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mentorship is important for the personal and professional development of a surgeon. Surgical mentoring includes technical and non-technical skills necessary for clinical activities, career improvement, leadership acquisition and research development. Mentors are important in different phases of surgical career, conferring various forms of support. The most delicate period for a surgeon is the transition between the role of trainee and physician, and the first few years are crucial to the trajectory of future career. While in the past, the main limitation for mentorship opportunities was the lack of available mentors at a single institution, more recently, long-distance mentorship opportunities have overcome this barrier. This is of particular importance for women and underrepresented minorities in surgery, who benefit the most from same gender and same ethnicity role model. Furthermore, having the opportunity to establish productive relationships with mentors from other institutions and/or countries will prevent the possibility of leading to dependence between mentee and mentor within a single institution. This review aims to investigate different forms of mentorships, with a specific interest in early career support, long-distance mentorship and opportunities for underrepresented minorities in surgery.
{"title":"Mentorship and early career mentorship","authors":"L. Ferrari, V. Mari, G. Capelli, G. Spolverato","doi":"10.20517/ais.2022.16","DOIUrl":"https://doi.org/10.20517/ais.2022.16","url":null,"abstract":"Mentorship is important for the personal and professional development of a surgeon. Surgical mentoring includes technical and non-technical skills necessary for clinical activities, career improvement, leadership acquisition and research development. Mentors are important in different phases of surgical career, conferring various forms of support. The most delicate period for a surgeon is the transition between the role of trainee and physician, and the first few years are crucial to the trajectory of future career. While in the past, the main limitation for mentorship opportunities was the lack of available mentors at a single institution, more recently, long-distance mentorship opportunities have overcome this barrier. This is of particular importance for women and underrepresented minorities in surgery, who benefit the most from same gender and same ethnicity role model. Furthermore, having the opportunity to establish productive relationships with mentors from other institutions and/or countries will prevent the possibility of leading to dependence between mentee and mentor within a single institution. This review aims to investigate different forms of mentorships, with a specific interest in early career support, long-distance mentorship and opportunities for underrepresented minorities in surgery.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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