Towards expert-based speed-precision control in early simulator training for novice surgeons
This addresses the need for improved training efficiency and effectiveness for novice surgeons in medical simulation, though it appears incremental as it builds on existing ideas of performance monitoring and expert benchmarking.
The paper tackles the problem of novice surgeons focusing too much on speed during early simulator training, which can irreversibly harm their precision development, and proposes using AI to monitor and control individual speed-precision tradeoffs by comparing trainee statistics with expert benchmarks.
Simulator training for image guided surgical interventions would benefit from intelligent systems that detect the evolution of task performance, and take control of individual speed precision strategies by providing effective automatic performance feedback. At the earliest training stages, novices frequently focus on getting faster at the task. This may, as shown here, compromise the evolution of their precision scores, sometimes irreparably, if it is not controlled for as early as possible. Artificial intelligence could help make sure that a trainee reaches optimal individual speed accuracy tradeoff by monitoring individual performance criteria, detecting critical trends at any given moment in time, and alerting the trainee as early as necessary when to slow down and focus on precision, or when to focus on getting faster. It is suggested that, for effective benchmarking, individual training statistics of novices are compared with the statistics of an expert surgeon. The speed accuracy functions of novices trained in a large number of experimental sessions reveal differences in individual speed versus precision strategies, and clarify why such strategies should be automatically detected and controlled for before further training on specific surgical task models, or clinical models, may be envisaged. How expert benchmark statistics may be exploited for automatic performance control is explained.