BIO

I am a second-year student at Florida State University, majoring in Clinical Professions within the College of Medicine's Interdisciplinary Medical Sciences Program. After completing my undergraduate studies, I plan to attend medical school and pursue a career as a physician.

Providing Feedback for Surgical Training and Assessment using Artificial Neural Networks

Abstract

Up to 100,000 deaths occur annually from preventable surgical errors due to subjective, time-consuming training and assessments. This study proposes that the development of an objective, time-efficient, and automated system of artificial intelligence (AI) techniques be integrated into the surgical education process to correct the current biases hindering surgical success. A surgical rubric, designed to collect consistent, structured feedback from experts (surgeons) with both categorical and open-ended responses was created, alongside the collection of high-quality laparoscopic suturing videos under the Fundamentals of Laparoscopic Surgery (FLS) program. Tool motion sequences were extracted from the videos and utilized to predict surgeons' multiple-choice feedback, using am AI model. A Proof of Concept (PoC) was demonstrated to further validate the methodology for predicting multiple-choice feedback. Going forward, this research lab aims to create an open-source, Large-Language Model (LLM) that uses open-ended feedback for personalized feedback per trial, without predefined categories.

Keywords: Biomedical Engineering, AI, Surgical Feedback, Machine Learning