REFERENCES
1. Lancet Digital Health. All things being equal: diversity in STEM. Lancet Digit Health 2020;2:e149.
2. Available from: http://www.dati.salute.gov.it/dati/dettaglioDataset.jsp?menu=dati&idPag=62 [Last accessed on 24 Aug 2021].
3. Barnes KL, Dunivan G, Sussman AL, McGuire L, McKee R. Behind the mask: an exploratory assessment of female surgeons' experiences of gender bias. Acad Med 2020;95:1529-38.
4. Hutchison K. Four types of gender bias affecting women surgeons and their cumulative impact. J Med Ethics 2020;46:236-41.
5. 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.
6. Kramer M, Heyligers IC, Könings KD. Implicit gender-career bias in postgraduate medical training still exists, mainly in residents and in females. BMC Med Educ 2021;21:253.
7. Ross SB, Jadick MF, Spence J, DeReus H, Sucandy I, Rosemurgy AS. Men surgeons' perceptions of women surgeons: is there a bias against women in surgery? Surg Endosc 2020;34:5122-31.
8. Bellini MI, Graham Y, Hayes C, Zakeri R, Parks R, Papalois V. A woman's place is in theatre: women's perceptions and experiences of working in surgery from the Association of Surgeons of Great Britain and Ireland women in surgery working group. BMJ Open 2019;9:e024349.
9. 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.
10. 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.
11. Kawahara D, Murakami Y, Tani S, Nagata Y. A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning. Br J Radiol 2021;94:20210525.
12. Li Q, Xiao Q, Li J, Wang Z, Wang H, Gu Y. Value of machine learning with multiphases CE-MRI radiomics for early prediction of pathological complete response to neoadjuvant therapy in HER2-positive invasive breast cancer. Cancer Manag Res 2021;13:5053-62.
13. Cheng K, You J, Wu S, et al. Artificial intelligence-based automated laparoscopic cholecystectomy surgical phase recognition and analysis. Surg Endosc 2021; doi: 10.1007/s00464-021-08619-3.
14. Madani A, Namazi B, Altieri MS, et al. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg 2020; doi: 10.1097/SLA.0000000000004594.
15. Battaglia E, Boehm J, Zheng Y, Jamieson AR, Gahan J, Majewicz Fey A. Rethinking autonomous surgery: focusing on enhancement over autonomy. Eur Urol Focus 2021;S2405-4569(21)00171.
16. Todd AR, Cawthorn TR, Temple-Oberle C. Pregnancy and parenthood remain challenging during surgical residency: a systematic review. Acad Med 2020;95:1607-15.
17. 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.
18. 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.
19. Anvari M, Manoharan B, Barlow K. From telementorship to automation. J Surg Oncol 2021;124:246-9.
20. Pears M, Konstantinidis S. The future of immersive technology in global surgery education. Indian J Surg 2021;1-5.
21. Ip M. Technology not policy will help drive female consultant numbers higher. Bulletin 2020;102:134-7.