Evolving robotic surgery training and improving patient safety, with the integration of novel technologies.
3D printed models
Eye tracking
Machine learning
Patient safety
Proficiency-based progression
Robotic-assisted surgery
Surgical education
Telementoring
Training
Journal
World journal of urology
ISSN: 1433-8726
Titre abrégé: World J Urol
Pays: Germany
ID NLM: 8307716
Informations de publication
Date de publication:
Aug 2021
Aug 2021
Historique:
received:
01
06
2020
accepted:
21
09
2020
pubmed:
7
11
2020
medline:
11
1
2022
entrez:
6
11
2020
Statut:
ppublish
Résumé
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. 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. 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. 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.
Identifiants
pubmed: 33156361
doi: 10.1007/s00345-020-03467-7
pii: 10.1007/s00345-020-03467-7
pmc: PMC8405494
doi:
Types de publication
Journal Article
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
2883-2893Informations de copyright
© 2020. The Author(s).
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