Journal
WORLD JOURNAL OF UROLOGY
Volume 39, Issue 8, Pages 2883-2893Publisher
SPRINGER
DOI: 10.1007/s00345-020-03467-7
Keywords
Robotic-assisted surgery; Training; Surgical education; 3D printed models; Telementoring; Eye tracking; Patient safety; Proficiency-based progression; Machine learning
Categories
Funding
- EPSRC [EP/P012841/1] Funding Source: UKRI
Ask authors/readers for more resources
Robot-assisted surgery is increasingly adopted by various surgical specialties, but there are inherent risks in utilizing new technologies early in the learning curve. Standardized and validated training programs are crucial for safe introduction of these technologies.
Introduction Robot-assisted surgery is becoming increasingly adopted by multiple surgical specialties. There is evidence of inherent risks of utilising new technologies that are unfamiliar early in the learning curve. The development of standardised and validated training programmes is crucial to deliver safe introduction. In this review, we aim to evaluate the current evidence and opportunities to integrate novel technologies into modern digitalised robotic training curricula. Methods A systematic literature review of the current evidence for novel technologies in surgical training was conducted online and relevant publications and information were identified. Evaluation was made on how these technologies could further enable digitalisation of training. Results Overall, the quality of available studies was found to be low with current available evidence consisting largely of expert opinion, consensus statements and small qualitative studies. The review identified that there are several novel technologies already being utilised in robotic surgery training. There is also a trend towards standardised validated robotic training curricula. Currently, the majority of the validated curricula do not incorporate novel technologies and training is delivered with more traditional methods that includes centralisation of training services with wet laboratories that have access to cadavers and dedicated training robots. Conclusions Improvements to training standards and understanding performance data have good potential to significantly lower complications in patients. Digitalisation automates data collection and brings data together for analysis. Machine learning has potential to develop automated performance feedback for trainees. Digitalised training aims to build on the current gold standards and to further improve the 'continuum of training' by integrating PBP training, 3D-printed models, telementoring, telemetry and machine learning.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available