Background Technology-enhanced teaching and learning, including Artificial Intelligence (AI) applications, has started to evolve in surgical education. Hence, the purpose of this scoping review is to explore the current and future roles of AI in surgical education. Methods Nine bibliographic databases were searched from January 2010 to January 2021. Full-text articles were included if they focused on AI in surgical education. Results Out of 14,008 unique sources of evidence, 93 were included. Out of 93, 84 were conducted in the simulation setting, and 89 targeted technical skills. Fifty-six studies focused on skills assessment/classification, and 36 used multiple AI techniques. Also, increasing sample size, having balanced data, and using AI to provide feedback were major future directions mentioned by authors. Conclusions AI can help optimize the education of trainees and our results can help educators and researchers identify areas that need further investigation. 

Exploring the roles of artificial intelligence in surgical education: A scoping review

One of the major difficulties in activity recognition stems from the lack of a model of the world where activities and events are to be recognised. When the domain is fixed and repetitive we can manually include this information using some kind of ontology or set of constraints. On many occasions, however, there are many new situations for which only some knowledge is common and many other domain-specific relations have to be inferred. Humans are able to do this from short descriptions in natural language, describing the scene or the particular task to be performed. In this paper we apply a tool that extracts situation models and rules from natural language description to a series of exercises in a surgical domain, in which we want to identify the sequence of events that are not possible, those that are possible (but incorrect according to the exercise) and those that correspond to the exercise or plan expressed by the description in natural language. The preliminary results show that a large amount of valuable knowledge can be extracted automatically, which could be used to express domain knowledge and exercises description in languages such as event calculus that could help bridge these high-level descriptions with the low-level events that are recognised from videos.

Knowledge Extraction from Task Narratives

To explore artificial intelligence's impact on surgical education, highlighting its advantages and challenges. 

Impact of artificial intelligence on the training of general surgeons of the future: a scoping review of the advances and challenges

We present an event detection system in a laparoscopic surgery domain, as part of a more ambitious supervision by observation project. The system, which only requires the incorporation of two cameras in a laparoscopic training box, integrates several computer vision and machine learning techniques to detect the states and movements of the elements involved in the exercise. We compare the states detected by the system with the hand-labelled ground truth, using an exercise of the domain as example. We show that the system is able to detect the events accurately.

Low-level Event Detection System for Minimally-Invasive Surgery Training

Virtual surgery simulators enable surgeons to learn by themselves, shortening their learning curves. Virtual simulators offer an objective evaluation of the surgeon’s skills at the end of each training session. The considered evaluation parameters are based on the analysis of the surgeon’s gestures performed throughout the training session. Currently, this information is usually known by surgeons only at the end of the training session, but very limited during the training performance. In this paper, we present a novel method for automatic and interactive evaluation of the surgeon’s skills that is able to supervise inexperienced surgeons during their training session with surgical simulators. The method is based on the assumption that the sequence of gestures carried out by an expert surgeon in the simulator can be translated into a sequence (a character string) that should be reproduced by a novice surgeon during a training session. In this work, a string-matching algorithm has been modified to calculate the alignment and distance between the sequences of both expert and novice during the training performance. The results have shown that it is possible to distinguish between different skill levels at all times during the surgical training session. The main contribution of this paper is a method where the difference between an expert’s sequence of gestures and a novice’s ongoing sequence is used to guide inexperienced surgeons. This is possible by indicating to novices the gesture corrections to be applied during surgical training as continuous expert supervision would do.

/pdf/automatic-supervision-of-gestures-to-guide-novice-surgeons-4f564zhpzs.pdf

Automatic supervision of gestures to guide novice surgeons during training

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Automatic supervision of gestures to guide novice surgeons during training