The potential of artificial intelligence as an equalizer of gender disparity in surgical training and education
0Abstract
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.
Keywords
INTRODUCTION
Over the last decades, female enrollment in medical schools has steadily increased. However, this growth did not lead to a proportional rise in females studying surgical specialties, which seems unable to attract and retain many young female doctors[1]. Manifest and occult barriers still prevent young women from enrolment, promotion, training opportunities, and career progression in surgical departments[1-5].
Several factors are responsible for the limitations in recruitment and advancement of young female doctors in surgical specialties, such as social and cultural barriers, gender discrimination and harassment, surgical lifestyle, and the lack of mentors and same-gender role models[6]. The main perceived obstacles in achieving a satisfying surgical career seem to be societal stereotypes, gender-biased mentality, and the lack of an adequate work-life balance[7,8].
Despite discrimination and obstacles, no significant differences were shown between males and females in learning surgical skills, among neither medical students nor surgical trainees[9-11]. However, gender disparity was observed in self-confidence, self-evaluation, and perception of competence achieved: female surgical trainees more often undervalue their abilities, especially their technical skills[10,12,13]. Independence, operative exposure, and faculty opinions of female residents’ ability could be influenced by this attitude, constraining opportunities for female surgeons in training.
To break through the glass ceiling in training for women in surgery, many solutions have been proposed. An early education for students on gender discrimination, setting up formal and informal mechanisms for identifying and preventing inappropriate behavior, promoting flexible career and work patterns, and sponsoring female mentors and role models have been advocated as possible ways to reduce the gender gap in surgery. Although these suggestions seem beneficial, they have not been enacted, and significant results have not been achieved yet[14,15].
Artificial intelligence surgery (AIS) could play a role in facing the gender gap in training and education of surgical specialties. AIS studies how autonomously acting machines can understand, process, and perform interventional actions. Machine learning (ML), deep learning (DP), computer vision (CV), and natural language processing (NLP) are leading toward more autonomous actions in surgery, with diagnostic and therapeutic potential[16]. In this paper, we aim to analyze different applications of AIS for breaking down gender disparity in the enrollment and training of women in surgery.
MAIN TYPES OF DISCRIMINATION IN SURGICAL EDUCATION AND TRAINING
Barriers to the choice of a surgical career
Many studies have analyzed the reasons preventing women from choosing surgical specialties. The main deterrents for female doctors in pursuing a surgical career are the length of training, time to date or marry, time available to spend with family, finding a good time during residency to have a child, taking maternity leave during residency, and being too old after residency to have children[5].
Furthermore, the perception of gender-based discrimination, the presence of a glass ceiling, the opinion of surgery as a male-dominated field, inadequate flexibility during training, and a lack of mentors or female surgeons as role models were identified as other factors which dissuade women from choosing a surgical path[17].
Analyzing the main barriers to selecting surgery as a specialty for medical students from 75 countries, the Global Surgery Working Group identified difficult access to surgical training, long years of study, heavy workload, and the high costs of training as the main issues for students from low and low-middle income countries[18]. Moreover, this study revealed that female students from low and low-middle income countries were 40% less likely than their male colleagues to consider a surgical career when controlling for other factors[18].
Factors that can increase female doctors’ interest in surgery have been the topics of different studies. Early exposure to surgical specialties during medical school, mentorship, and role models were identified as the factors which increase the likelihood that female doctors will pursue a surgical specialty[19-23]. Additional ways to encourage women’s representation in surgical departments were flexible working patterns, shortened training time, improved sense of belonging, and better work-life balance[24-27].
Exposure to other women who have pursued a surgical career was considered one of the most inspirational reasons to induce young women to follow a surgical career[28]. Furthermore, suggestions and mentoring by fellow surgical residents were shown to be even more effective than mentorship by a faculty member[19]. Mentorship and role models were recognized as one of the principal factors in supporting interest in surgery[18,21,23,28].
Surgical training
During surgical training, women had to face several obstacles, such as gender disparity, inequity during the surgical residency application process and interview, imbalance in the bestowment of awards, sexual harassment, and discrimination in pregnancy experience and parental leave.
In pursuing a surgical career, female doctors had to deal with gender discrimination during the procedure of residency application, including recommendation letters[29-33], interviews[34-38], and fellowship applications[39,40]. Furthermore, female applicants required superior letters of recommendation to be given the same opportunity as male candidates[30]. However, when standardized letters of recommendation were requested, these disparities were not present[31].
During residency interviews, applicants frequently received questions about personal matters which were unrelated to medical school performance. Female respondents more frequently experienced a potentially illegal question compared to male applicants[36]. Women were recurrently asked about marital status, family planning, and maternity plans[37].
During surgical training, women experienced disparities in operative autonomy and evaluation, and interesting differences were also observed in self-evaluation[11,41-48]. A significant difference between male and female residents’ operative autonomy was observed during surgical training programs. Even though a gender disparity in residents’ performance was not demonstrated, women tended to underestimate their abilities compared to faculty assessment[46-48].
Female residents were revealed to be more likely to experience several other kinds of discrimination during their training, compared to their male colleagues. Female trainees more commonly experience stereotyping and discrimination, such as being mistaken for non-physicians, being subject to different standards of evaluation, and being victims of harassment[49-51].
During training, significant discrimination was also reported with pregnancy experience and family planning. Women were less likely to have children during surgical residency because of stigma, fear of modification of their fellowship program, and perception of missing out on a job opportunity[20,52-55]. In many countries, there is a lack of a formal policy for maternity leave or a maternity support program during residency. This deficiency is another issue reported as an obstacle in pursuing a surgical career[55,56].
All these barriers and discrimination experienced during surgical training led to a higher rate of burnout, depression, and suicidal thoughts among female surgical residents compared to male trainees[50,57-59]. Thus, female trainees were observed to be more likely to leave surgical residency[60-62].
AIS IN SURGICAL EDUCATION AND TRAINING FOR WOMEN
AI is the study of how computers can understand, process, and act autonomously in the real world[63]. In AIS, machines perform interventional actions[16].
ML is a field of AI in which computers reproduce the acts of learning and solving problems, improving their performance by learning from data[16,63]. AI models examine high volumes of data, and then offer accurate predictions for upcoming events based on the statistical analysis of previous associations, and they constantly improve with new data. Neural networks can develop over time thanks to incremental learning processes, going beyond standard software[16,63].
Natural language processing (NLP) is the interaction of AI and linguistics. NLP has advanced from essential approaches (e.g., word to word) through an evolved process of coding words, sentences, and contexts[64].
Thanks to the aforementioned research, AI has already been employed in several fields in surgery, especially to optimize patient management, support training, and improve surgical skills.
With AI, augmented reality offers enhanced vision by superimposing a digital image over the user’s view, while virtual reality allows interacting with a digitally created setting[65]. Surgical simulation based on virtual reality (VR) allows training and practice in a safe setting so residents can learn from their errors without harming patients[66]. In the last few years, several surgical simulators for different surgical specialties, procedures, and variants have been designed. The simpler ones are low-fidelity simulators that teach basic surgical procedures. For example, the MIST-VR system was developed to teach basic laparoscopic skills, suturing, and knot-tying[67]. High-fidelity VR systems include the Lap Mentor which incorporates over 65 cases in the fields of general surgery, gynecology, urology, and bariatric surgery[68,69]. The potential of VR for training and monitoring basic laparoscopic skills and full laparoscopic procedures is well recognized[70]. The routine use of surgical simulations can reduce operative times and complication rates, improving patient outcomes[66].
VR simulations offer real-time feedback to users about their performance within the simulation. They can evaluate time to complete a task, errors made during surgery, and the surgeon’s economy of movements[71], providing a method for skill evaluation that is objective and quantitative, not influenced by the gender of the operator. Although VR simulations are burdened by some disadvantages, such as high costs, lack of force feedback, and the limited realism of some simulation models[72], as VR technology advances, simulators are getting more cost-effective and more able to reproduce human anatomy. A relatively new development in training simulators is robot-assisted laparoscopic surgery simulators (RAS). Studies on Da Vinci simulators suggest that they reduce the console training time, although RAS simulators are burdened by high costs and a lack of high fidelity surgical simulations[73]. However, the Da Vinci Skills simulator could be a feasible tool for the evaluation of RAS skills and credentialing of RAS surgeons, allowing them to obtain an objective assessment of technical skills that are not prejudiced by the surgeon’s gender[74]. The development of simulators for RAS is only beginning, and technological improvement may permit the development of cheaper and better systems in the future.
AI may have further employment in supporting female surgeon training and facilitating their surgical career, by facing gender barriers and disparity.
AI is useful even during the selection process of surgical residency programs. Sarraf et al. demonstrated that AI with NLP can identify linguistic differences and gender disparity in letters of recommendation for general surgery residency applicants[75]. AI could detect implicit biases in female applicants’ selection and thus avoid them, in order to obtain equal resident selection.
During surgical training, the importance of one-on-one mentoring was demonstrated for female trainees, who were shown to be particularly receptive to this kind of approach[10]. The integration of AI and robotic surgery could be employed to provide remote surgical mentoring and training to surgical residents, transferring surgical skills and knowledge. Telerobotic surgery has firmly demonstrated the feasibility and clinical safety of remote telementoring in surgery[76-78]. Telementoring has been tested as a training method for several laparoscopic procedures, such as cholecystectomy[79], adrenalectomy[80], colon surgery[81], and bariatric surgery[82]. However, more studies are needed to confirm the same effectiveness of telementoring as an educational intervention compared to on-site mentoring[83]. However, telementoring has already been successfully used in surgical training in rural areas of Canada[84]. Moreover, it may have a role in extending the possibility of training surgical residents in low-and middle-income countries, reducing the significant limitations due to travelling[85].
Promising perspectives also seem to be offered by robotic telementoring. For instance, it has been shown that telerobotic-assisted colorectal surgery is feasible and safe for patients, and it is an effective tool for supporting surgeons during the learning curve[86]. Similar evidence was provided in neurosurgery by Mendez et al., who completed six long-distance robotic-assisted telementoring neurosurgical procedures[87]. Thus, robotic-assisted telementoring could also potentially facilitate the teaching of advanced surgical skills worldwide.
For female trainees, telementoring could also allow communicating remotely with same-sex role models and obtaining mentoring and counselling, even in centers with few female surgeons who can fit this role. This approach could not only help women to improve their surgical technical skills but also help raise awareness of gender issues and how to deal with them during surgical training[88].
Similar to how virtual reality and robotic surgery can be used to train surgeons at work, this solution may be used for trainees on maternity leave to continue their training and avoid losing their technical skills. Cost, size of the machine, and limited functionality are problematic, which might be eliminated or reduced by developing new technologies. Therefore, the possibility provided by AIS to train anywhere and anytime with virtual reality and simulators in surgery could significantly contribute to ameliorating female surgeons’ work–life balance issues[89].
The positive effect of AI on training, education, and remote mentoring could favorably impact the choice of young women to pursue a surgical career. AI could help female surgical trainees in facing many of the above-mentioned obstacles, so they would not be forced to choose between their careers and personal life.
Furthermore, all these facilities could contribute to reducing the rate of female surgeons who abandon their training and profession because of difficulties due to gender disparity and discrimination[90].
However, there are some open issues in the everyday application of AI in training and education in surgery. Potential disadvantages of routine employment of AI in surgical training are high costs and technical requirements[91]. Obstacles to the implementation of telementoring include poor video signal due to bandwidth or latency, loss of transmission, and poor audio quality[92]. Furthermore, hospital licensing and credentialing might be required, creating an additional limitation to the introduction of AI in surgical training[83].
Furthermore, AI is based on enormous datasets, so the management and cybersecurity of personal data is a critical issue for the application of AI[93]. Finally, there are ethical problems related to AI application in surgical training[93]. For instance, there might be an ethical issue related to the responsibility of the surgeon who operates and teaches from a distant location, possibly in another country, thanks to telementoring[77].
CONCLUSION
AIS permits remote training and telementoring and could improve female surgeons’ education worldwide. AI could contribute to breaking down gender disparity in surgical training, and, consequently, it could encourage women to choose a surgical career. Many surgical tools have been created and tested on male surgeons (especially laparoscopic instruments and staplers)[94-96]; future studies should develop instruments considering gender differences. The development of systematic AI-based training and education programs could encourage women to choose a surgical career and help break down gender disparity during surgical training.
DECLARATIONS
Authors’ contributionsStudy concept and design: Ferrari L, Mari V
Drafting of manuscript: Mari V
Critical revision: Ferrari L, Spolverato G
Availability of data and materialsNot applicable.
Financial support and sponsorshipNone.
Conflicts of interestAll authors declared that there are no conflicts of interest.
Ethical approval and consent to participateNot applicable.
Consent for publicationNot applicable.
Copyright© The Author(s) 2022.
REFERENCES
1. Stephens EH, Heisler CA, Temkin SM, Miller P. The current status of women in surgery: how to affect the future. JAMA Surg 2020;155:876-85.
2. Salles A, Awad M, Goldin L, et al. Estimating implicit and explicit gender bias among health care professionals and surgeons. JAMA Netw Open 2019;2:e196545.
3. Ferrari L, Mari V, Parini S, et al. Discrimination toward women in surgery: a systematic scoping review. Ann Surg 2022;276:1-8.
4. Ferrari L, Mari V, De Santi G, et al. Early barriers to career progression of women in surgery and solutions to improve them: a systematic scoping review. Ann Surg 2022; doi: 10.1097/SLA.0000000000005510.
5. Larsen AM, Pories S, Parangi S, Robertson FC. Barriers to pursuing a career in surgery: an institutional survey of harvard medical school students. Ann Surg 2021;273:1120-6.
6. Trinh LN, O’Rorke E, Mulcahey MK. Factors influencing female medical students’ decision to pursue surgical specialties: a systematic review. J Surg Educ 2021;78:836-49.
7. Are C, Stoddard HA, O’Holleran B, Thompson JS. A multinational perspective on “lifestyle” and other perceptions of contemporary medical students about general surgery. Ann Surg 2012;256:378-86.
8. Deedar-Ali-Khawaja R, Khan SM. Trends of surgical career selection among medical students and graduates: a global perspective. J Surg Educ 2010;67:237-48.
9. Burgos CM, Josephson A. Gender differences in the learning and teaching of surgery: a literature review. Int J Med Educ 2014;5:110-24.
10. Ali A, Subhi Y, Ringsted C, Konge L. Gender differences in the acquisition of surgical skills: a systematic review. Surg Endosc 2015;29:3065-73.
11. White MT, Welch K. Does gender predict performance of novices undergoing Fundamentals of Laparoscopic Surgery (FLS) training? Am J Surg 2012;203:397-400; discussion 400.
12. Babchenko O, Gast K. Should we train female and male residents slightly differently? JAMA Surg 2020;155:373-4.
13. Kolozsvari NO, Andalib A, Kaneva P, et al. Sex is not everything: the role of gender in early performance of a fundamental laparoscopic skill. Surg Endosc 2011;25:1037-42.
14. Hirayama M, Fernando S. Organisational barriers to and facilitators for female surgeons’ career progression: a systematic review. J R Soc Med 2018;111:324-34.
15. Zhuge Y, Kaufman J, Simeone DM, Chen H, Velazquez OC. Is there still a glass ceiling for women in academic surgery? Ann Surg 2011;253:637-43.
16. Gumbs AA, Frigerio I, Spolverato G, et al. Artificial intelligence surgery: how do we get to autonomous actions in surgery? Sensors (Basel) 2021;21:5526.
17. Fitzgerald JE, Tang SW, Ravindra P, Maxwell-Armstrong CA. Gender-related perceptions of careers in surgery among new medical graduates: results of a cross-sectional study. Am J Surg 2013;206:112-9.
18. Marks IH, Diaz A, Keem M, et al. Barriers to women entering surgical careers: a global study into medical student perceptions. World J Surg 2020;44:37-44.
19. Ng CWQ, Syn NL, Hussein RBM, Ng M, Kow AWC. Push and pull factors, and the role of residents in nurturing medical students’ interest in surgery as a career option in a multicultural Asian context: results of a prospective national cohort study. Am J Surg 2020;220:1549-56.
20. Rogers AC, Wren SM, McNamara DA. Gender and specialty influences on personal and professional life among trainees. Ann Surg 2019;269:383-7.
21. Altieri MS, Price KL, Yang J, Jones DB, Pryor AD. What are women being advised by mentors when applying to surgery? Am Surg 2020;86:266-72.
22. Park J, Minor S, Taylor RA, Vikis E, Poenaru D. Why are women deterred from general surgery training? Am J Surg 2005;190:141-6.
23. Faucett EA, McCrary HC, Milinic T, Hassanzadeh T, Roward SG, Neumayer LA. The role of same-sex mentorship and organizational support in encouraging women to pursue surgery. Am J Surg 2017;214:640-4.
24. Harris LM, Chaikof EL, Eidt JF. Altering the career choice: can we attract more women to vascular surgery? J Vasc Surg 2007;45:846-8.
25. Kleinert R, Fuchs C, Romotzky V, et al. Generation Y and surgical residency - Passing the baton or the end of the world as we know it? PLoS One 2017;12:e0188114.
26. Walker NR, Deekonda P, Glasbey JC, et al. Association of Surgeons in Training. Attracting medical students and doctors into surgical training in the UK and Ireland. Int J Surg 2019;67:107-12.
27. Shin SH, Tang GL, Shalhub S. Integrated residency is associated with an increase in women among vascular surgery trainees. J Vasc Surg 2020;71:609-15.
28. Ng CWQ, Syn NL, Hussein RBM, Ng M, Kow AWC. Factors attracting or deterring female medical students in asia from pursuing a surgical career, and the impact of surgical clerkship, mentorship, and role models: a multicultural asian perspective from a national prospective cohort study. J Surg Res 2021;260:200-9.
29. Turrentine FE, Dreisbach CN, St Ivany AR, Hanks JB, Schroen AT. Influence of gender on surgical residency applicants’ recommendation letters. J Am Coll Surg 2019;228:356-365.e3.
30. Dream S, Olivet MM, Tanner L, Chen H. Do male chairs of surgery have implicit gender bias in the residency application process? Am J Surg 2021;221:697-700.
31. Powers A, Gerull KM, Rothman R, Klein SA, Wright RW, Dy CJ. Race- and gender-based differences in descriptions of applicants in the letters of recommendation for orthopaedic surgery residency. JB JS Open Access 2020;5:e20.
32. Friedman R, Fang CH, Hasbun J, et al. Use of standardized letters of recommendation for otolaryngology head and neck surgery residency and the impact of gender. Laryngoscope 2017;127:2738-45.
33. Filippou P, Mahajan S, Deal A, et al. The presence of gender bias in letters of recommendations written for urology residency applicants. Urology 2019;134:56-61.
34. Lee JS, Ji YD, Kushner H, Kaban LB, Peacock ZS. Residency interview experiences in oral and maxillofacial surgery differ by gender and affect residency ranking. J Oral Maxillofac Surg 2019;77:2179-95.
35. Harkin E, Murphy M, Liskutin T, Nystrom L, Wu K, Schiff A. Discriminatory questions asked during residency programme interviews: perspective from both interviewers and applicants. Postgrad Med J 2021;97:355-62.
36. Hern HG Jr, Trivedi T, Alter HJ, Wills CP. How prevalent are potentially illegal questions during residency interviews? Acad Med 2016;91:1546-53.
37. Fereydooni A, Ramirez JL, Morrow KL, et al. Interview experience, post-interview communication, and gender-based differences in the integrated vascular surgery residency match. J Vasc Surg 2022;75:316-322.e2.
38. Bohl DD, Iantorno SE, Kogan M. Inappropriate questions asked of female orthopaedic surgery applicants from 1971 to 2015: a cross-sectional study. J Am Acad Orthop Surg 2019;27:519-26.
39. Hoffman A, Grant W, McCormick M, Jezewski E, Matemavi P, Langnas A. Gendered differences in letters of recommendation for transplant surgery fellowship applicants. J Surg Educ 2019;76:427-32.
40. Hoffman A, Ghoubrial R, McCormick M, Matemavi P, Cusick R. Exploring the gender gap: letters of recommendation to pediatric surgery fellowship. Am J Surg 2020;219:932-6.
41. Hoops H, Heston A, Dewey E, Spight D, Brasel K, Kiraly L. Resident autonomy in the operating room: does gender matter? Am J Surg 2019;217:301-5.
42. Meyerson SL, Odell DD, Zwischenberger JB, et al. Procedural learning and safety collaborative. The effect of gender on operative autonomy in general surgery residents. Surgery 2019;166:738-43.
43. Lane SM, Young KA, Hayek SA, et al. Meaningful autonomy in general surgery training: exploring for gender bias. Am J Surg 2020;219:240-4.
44. Bucholz EM, Sue GR, Yeo H, Roman SA, Bell RH Jr, Sosa JA. Our trainees’ confidence: results from a national survey of 4136 US general surgery residents. Arch Surg 2011;146:907-14.
45. Gerull KM, Loe M, Seiler K, McAllister J, Salles A. Assessing gender bias in qualitative evaluations of surgical residents. Am J Surg 2019;217:306-13.
46. Minter RM, Gruppen LD, Napolitano KS, Gauger PG. Gender differences in the self-assessment of surgical residents. Am J Surg 2005;189:647-50.
47. Cooney CM, Aravind P, Lifchez SD, et al. Differences in operative self-assessment between male and female plastic surgery residents: a survey of 8149 cases. Am J Surg 2021;221:799-803.
48. Flyckt RL, White EE, Goodman LR, Mohr C, Dutta S, Zanotti KM. The use of laparoscopy simulation to explore gender differences in resident surgical confidence. Obstet Gynecol Int 2017;2017:1945801.
49. Nukala M, Freedman-Weiss M, Yoo P, Smeds MR. Sexual harassment in vascular surgery training programs. Ann Vasc Surg 2020;62:92-7.
50. Hu YY, Ellis RJ, Hewitt DB, et al. Discrimination, abuse, harassment, and burnout in surgical residency training. N Engl J Med 2019;381:1741-52.
51. Freedman-Weiss MR, Chiu AS, Heller DR, et al. Understanding the barriers to reporting sexual harassment in surgical training. Ann Surg 2020;271:608-13.
52. Rangel EL, Smink DS, Castillo-Angeles M, et al. Pregnancy and motherhood during surgical training. JAMA Surg 2018;153:644-52.
53. Rangel EL, Lyu H, Haider AH, Castillo-Angeles M, Doherty GM, Smink DS. Factors associated with residency and career dissatisfaction in childbearing surgical residents. JAMA Surg 2018;153:1004-11.
54. Rangel EL, Castillo-Angeles M, Changala M, Haider AH, Doherty GM, Smink DS. Perspectives of pregnancy and motherhood among general surgery residents: a qualitative analysis. Am J Surg 2018;216:754-9.
55. Castillo-Angeles M, Smink DS, Rangel EL. Perspectives of US general surgery program directors on cultural and fiscal barriers to maternity leave and postpartum support during surgical training. JAMA Surg 2021;156:647-53.
56. Merchant SJ, Hameed SM, Melck AL. Pregnancy among residents enrolled in general surgery: a nationwide survey of attitudes and experiences. Am J Surg 2013;206:605-10.
57. Salles A, Mueller CM, Cohen GL. Exploring the relationship between stereotype perception and residents’ well-being. J Am Coll Surg 2016;222:52-8.
58. Salles A, Milam L, Cohen G, Mueller C. The relationship between perceived gender judgment and well-being among surgical residents. Am J Surg 2018;215:233-7.
59. Dahlke AR, Johnson JK, Greenberg CC, et al. Gender differences in utilization of duty-hour regulations, aspects of burnout, and psychological well-being among general surgery residents in the United States. Ann Surg 2018;268:204-11.
60. Ellis RJ, Holmstrom AL, Hewitt DB, et al. A comprehensive national survey on thoughts of leaving residency, alternative career paths, and reasons for staying in general surgery training. Am J Surg 2020;219:227-32.
62. Gifford E, Galante J, Kaji AH, et al. Factors associated with general surgery residents’ desire to leave residency programs: a multi-institutional study. JAMA Surg 2014;149:948-53.
63. Ward TM, Mascagni P, Madani A, Padoy N, Perretta S, Hashimoto DA. Surgical data science and artificial intelligence for surgical education. J Surg Oncol 2021;124:221-30.
64. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc 2011;18:544-51.
65. Badash I, Burtt K, Solorzano CA, Carey JN. Innovations in surgery simulation: a review of past, current and future techniques. Ann Transl Med 2016;4:453.
66. Zevin B, Aggarwal R, Grantcharov TP. Surgical simulation in 2013: why is it still not the standard in surgical training? J Am Coll Surg 2014;218:294-301.
67. Wilson MS, Middlebrook A, Sutton C, Stone R, McCloy RF. MIST VR: a virtual reality trainer for laparoscopic surgery assesses performance. Ann R Coll Surg Engl 1997;79:403-4.
68. Ayodeji ID, Schijven M, Jakimowicz J, Greve JW. Face validation of the Simbionix LAP Mentor virtual reality training module and its applicability in the surgical curriculum. Surg Endosc 2007;21:1641-9.
69. Elessawy M, Wewer A, Guenther V, et al. Validation of psychomotor tasks by Simbionix LAP Mentor simulator and identifying the target group. Minim Invasive Ther Allied Technol 2017;26:262-8.
70. Alaker M, Wynn GR, Arulampalam T. Virtual reality training in laparoscopic surgery: a systematic review & meta-analysis. Int J Surg 2016;29:85-94.
71. Satava RM. Historical review of surgical simulation - a personal perspective. World J Surg 2008;32:141-8.
73. Liu M, Curet M. A review of training research and virtual reality simulators for the da Vinci surgical system. Teach Learn Med 2015;27:12-26.
75. Sarraf D, Vasiliu V, Imberman B, Lindeman B. Use of artificial intelligence for gender bias analysis in letters of recommendation for general surgery residency candidates. Am J Surg 2021;222:1051-9.
76. Anvari M, Manoharan B, Barlow K. From telementorship to automation. J Surg Oncol 2021;124:246-9.
77. Antoniou SA, Antoniou GA, Franzen J, et al. A comprehensive review of telementoring applications in laparoscopic general surgery. Surg Endosc 2012;26:2111-6.
78. Bilgic E, Turkdogan S, Watanabe Y, et al. Effectiveness of telementoring in surgery compared with on-site mentoring: a systematic review. Surg Innov 2017;24:379-85.
79. Byrne JP, Mughal MM. Telementoring as an adjunct to training and competence-based assessment in laparoscopic cholecystectomy. Surg Endosc 2000;14:1159-61.
80. Bruschi M, Micali S, Porpiglia F, et al. Laparoscopic telementored adrenalectomy: the Italian experience. Surg Endosc 2005;19:836-40.
81. Schlachta CM, Sorsdahl AK, Lefebvre KL, McCune ML, Jayaraman S. A model for longitudinal mentoring and telementoring of laparoscopic colon surgery. Surg Endosc 2009;23:1634-8.
82. Fuertes-Guiró F, Vitali-Erion E, Rodriguez-Franco A. A program of telementoring in laparoscopic bariatric surgery. Minim Invasive Ther Allied Technol 2016;25:8-14.
83. Erridge S, Yeung DKT, Patel HRH, Purkayastha S. Telementoring of surgeons: a systematic review. Surg Innov 2019;26:95-111.
84. Sebajang H, Trudeau P, Dougall A, Hegge S, McKinley C, Anvari M. Telementoring: an important enabling tool for the community surgeon. Surg Innov 2005;12:327-31.
85. Pears M, Konstantinidis S. The future of immersive technology in global surgery education. Indian J Surg 2021:1-5.
86. Sebajang H, Trudeau P, Dougall A, Hegge S, McKinley C, Anvari M. The role of telementoring and telerobotic assistance in the provision of laparoscopic colorectal surgery in rural areas. Surg Endosc 2006;20:1389-93.
87. Mendez I, Hill R, Clarke D, Kolyvas G, Walling S. Robotic long-distance telementoring in neurosurgery. Neurosurgery 2005;56:434-40; discussion 434.
88. Georgi M, Morka N, Patel S, et al. The impact of same gender speed-mentoring on women’s perceptions of a career in surgery - a prospective cohort study. J Surg Educ 2022; doi: 10.1016/j.jsurg.2022.05.014.
89. Ip M. Technology not policy will help drive female consultant number higher. Bulletin 2020;102:134-7.
90. Maehara T, Kamiya K, Fujimaki T, et al. Gender Equality Committee of the Japan Neurosurgical Society. A questionnaire to assess the challenges faced by women who quit working as full-time neurosurgeons. World Neurosurg 2020;133:331-42.
91. Park CW, Seo SW, Kang N, et al. Artificial intelligence in health care: current applications and issues. J Korean Med Sci 2020;35:e379.
92. Bruns NE, Irtan S, Rothenberg SS, Bogen EM, Kotobi H, Ponsky TA. Trans-Atlantic telementoring with pediatric surgeons: technical considerations and lessons learned. J Laparoendosc Adv Surg Tech A 2016;26:75-8.
93. O’Sullivan S, Nevejans N, Allen C, et al. Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery. Int J Med Robot 2019;15:e1968.
94. Kono E, Tada M, Kouchi M, et al. Ergonomic evaluation of a mechanical anastomotic stapler used by Japanese surgeons. Surg Today 2014;44:1040-7.
95. Sutton E, Irvin M, Zeigler C, Lee G, Park A. The ergonomics of women in surgery. Surg Endosc 2014;28:1051-5.
Cite This Article
Export citation file: BibTeX | RIS
OAE Style
Mari V, Spolverato G, Ferrari L. The potential of artificial intelligence as an equalizer of gender disparity in surgical training and education. Art Int Surg 2022;2:122-31. http://dx.doi.org/10.20517/ais.2022.12
AMA Style
Mari V, Spolverato G, Ferrari L. The potential of artificial intelligence as an equalizer of gender disparity in surgical training and education. Artificial Intelligence Surgery. 2022; 2(3): 122-31. http://dx.doi.org/10.20517/ais.2022.12
Chicago/Turabian Style
Mari, Valentina, Gaya Spolverato, Linda Ferrari. 2022. "The potential of artificial intelligence as an equalizer of gender disparity in surgical training and education" Artificial Intelligence Surgery. 2, no.3: 122-31. http://dx.doi.org/10.20517/ais.2022.12
ACS Style
Mari, V.; Spolverato G.; Ferrari L. The potential of artificial intelligence as an equalizer of gender disparity in surgical training and education. Art. Int. Surg. 2022, 2, 122-31. http://dx.doi.org/10.20517/ais.2022.12
About This Article
Special Issue
Copyright
Data & Comments
Data
0
Cite This Article 8 clicks
Like This Article 28
likes
Comments
Comments must be written in English. Spam, offensive content, impersonation, and private information will not be permitted. If any comment is reported and identified as inappropriate content by OAE staff, the comment will be removed without notice. If you have any queries or need any help, please contact us at support@oaepublish.com.