Anne van Dalen

232 I Chapter 8 IEEE InternationalConferenceonRobotics and Automation; 9-13May 2011; Shanghai, China. IEEE. 33. Mayer H, Gomez F, Wierstra D, et al. A system for robotic heart surgery that learns to tie knots using recurrent neural networks. Adv Robot. 2008;22(13- 14):1521-1537. 34. Nishihara S, Sugano N, Ikai M, et al. Accuracy evaluation of a shape-based registration method for a computer navigation system for total knee arthroplasty. J Knee Surg. 2003;16(2): 98- 105 35. Alami H, Lehoux P, Auclair Y, et al. Artificial intelligence and health technology assessment: Anticipating a new level of complexity. J Med Internet Res. 2020;22(7):e17707. 36. Rogers EM. Diffusion of Innovations. New York City, NY: Simon & Schuster; 2010. 37. Sahin I. Detailed review of Rogers’ diffusion of innovations theory and educational technology-related studies based on Rogers’ theory. TOJET. 2006;5(2):14-23. 38. Dalen ASHM, Legemaate J, Schlack WS, Legemate DA, Schijven MP. Legal perspectives on black box recording devices in the operating environment. Br J Surg. 2019; 106(0):1433. doi:10.1002/ bjs.11198 39. Pesapane F, Volonte C, Codari M, Sardanelli F. Arti ´ ficial intelligence as a medical device in radiology: Ethical and regulatory issues in Europe and the United States. Insights Imaging. 2018;9(5):745- 753. doi:10.1007/s13244-018- 0645-y 40. MacNeil M, Koch M, Kuspinar A, Juzwishin D, Lehoux P, Stolee P. Enabling health technology innovation in Canada: Barriers and facilitators in policy and regulatory processes. Health Pol. 2019;123(2):203-214. 41. Cath C. Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Phil Trans Math Phys Eng Sci. 2018;376:20180080. doi:10.1098/rsta.2018. 0080 42. Bejnordi BE, Veta M, Van Diest PJ, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017; 318(22):2199-2210. 43. Somashekhar SP, Sepulveda M-J, Puglielli S, et al. Watson ´ for oncology and breast cancer treatment recommendations: Agreement with an expert multidisciplinary tumor board. Ann Oncol. 2018;29(2):418- 423. 44. Rahman SA, Walker RC, Lloyd MA, et al. Machine learning to predict early recurrence after oesophageal cancer surgery. BJS. 2020;107(8):1042-1052. doi:10.1002/bjs.11461 45. Buzaev IV, Plechev VV, Nikolaeva IE, Galimova RM. Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes. Chronic Dis Transl Med. 2016; 2(3):166- 172. 46. Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset. J Am Med Inf Assoc. 2016;24(2):361-370. 47. Slomka PJ, Dey D, Sitek A, Motwani M, Berman DS, Germano G. Cardiac imaging: Working towards fullyautomated machine analysis &