  Telementoring surgeons as they perform surgery can be essential in the
treatment of patients when in situ expertise is not available. Nonetheless,
expert mentors are often unavailable to provide trainees with real-time medical
guidance. When mentors are unavailable, a fallback autonomous mechanism should
provide medical practitioners with the required guidance. However,
AI/autonomous mentoring in medicine has been limited by the availability of
generalizable prediction models, and surgical procedures datasets to train
those models with. This work presents the initial steps towards the development
of an intelligent artificial system for autonomous medical mentoring.
Specifically, we present the first Database for AI Surgical Instruction
(DAISI). DAISI leverages on images and instructions to provide step-by-step
demonstrations of how to perform procedures from various medical disciplines.
The dataset was acquired from real surgical procedures and data from academic
textbooks. We used DAISI to train an encoder-decoder neural network capable of
predicting medical instructions given a current view of the surgery.
Afterwards, the instructions predicted by the network were evaluated using
cumulative BLEU scores and input from expert physicians. According to the BLEU
scores, the predicted and ground truth instructions were as high as 67%
similar. Additionally, expert physicians subjectively assessed the algorithm
using Likert scale, and considered that the predicted descriptions were related
to the images. This work provides a baseline for AI algorithms to assist in
autonomous medical mentoring.
