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Das, A., Khan, D.Z., Williams, S.C., Hanrahan, J.G., Borg, A., Dorward, N.L., et al. (2023) A Multi-Task Network for Anatomy Identification in Endoscopic Pituitary Surgery. In: Greenspan, H., et al., Eds., Medical Image Computing and Computer Assisted Intervention—MICCAI 2023, Springer, 472-482.
https://doi.org/10.1007/978-3-031-43996-4_45

has been cited by the following article:

• TITLE: A Prototype AI Surgical Assistant for Real-Time Consultation during Laparoscopic Surgery

  AUTHORS: Savvas Hirides, Petros Hirides, Kalliopi Kouloufakou, Constantinos Hirides

  KEYWORDS: AI Surgical Assistant, Real-Time Video Analysis, GPT-4o in Surgery, AI in the OR, Voice-Activated Intraoperative Support

  JOURNAL NAME: Surgical Science, Vol.16 No.7, July 31, 2025

  ABSTRACT: Recent advancements in generative AI and large language models (LLMs) have sparked new opportunities in surgical innovation. We present our prototype AI Surgical Assistant Prototype System, integrating real-time vision support for streaming video of the operative field, advanced speech recognition, multilingual natural voice interaction, both long- and short-term memory simulation, and customizable behavioral profiles. Testing showed that the system exhibited contextual awareness from visual feedback in 38% of instances, ability for verbal interaction, and dynamic memory logging throughout the procedure. This feasibility study suggests that real-time, context-aware AI support is technically viable in the OR and may serve as a basis for future clinical models.