LLMs Enable Context-Aware Augmented Reality in Surgical Navigation

TL;DR

This paper introduces a novel LLM-based voice-controlled interface for surgical AR systems, demonstrating improved usability, reduced task time, and lower cognitive load in pancreatic surgeries compared to traditional speech commands.

Contribution

It presents the first integration of Large Language Models into surgical AR interfaces, enhancing usability and decision-making in complex surgical procedures.

Findings

LLM-based VCUI reduces task completion time.

Lower cognitive workload with LLM-based VCUI.

Surgeons prefer LLM-based VCUI for its intuitiveness.

Abstract

Wearable Augmented Reality (AR) technologies are gaining recognition for their potential to transform surgical navigation systems. As these technologies evolve, selecting the right interaction method to control the system becomes crucial. Our work introduces a voice-controlled user interface (VCUI) for surgical AR assistance systems (ARAS), designed for pancreatic surgery, that integrates Large Language Models (LLMs). Employing a mixed-method research approach, we assessed the usability of our LLM-based design in both simulated surgical tasks and during pancreatic surgeries, comparing its performance against conventional VCUI for surgical ARAS using speech commands. Our findings demonstrated the usability of our proposed LLM-based VCUI, yielding a significantly lower task completion time and cognitive workload compared to speech commands. Additionally, qualitative insights from interviews with surgeons aligned with the quantitative data, revealing a strong preference for the LLM-based VCUI. Surgeons emphasized its intuitiveness and highlighted the potential of LLM-based VCUI in expediting decision-making in surgical environments.