Augmenting Biomedical Image Segmentation with Large Language Model- Interfaces: Enhancing Usability and Diagnostic Insights

Biomedical image segmentation has revolutionized medical diagnostics and research, offering unprecedented precision in analyzing complex anatomical structures. However, challenges like complex data interpretation, limited accessibility for non-experts, and significant computational costs restrict its broader utility. This paper introduces an innovative framework integrating large language models (LLMs), such as GPT, with advanced segmentation systems, quantum databases, and optimized image compression techniques. This hybrid approach not only enhances interpretability and usability through natural language queries but also accelerates data processing and optimizes storage and transmission costs. Numerical simulations demonstrate improved segmentation efficiency, faster diagnostic timelines, and greater user satisfaction, underscoring the transformative potential of this system in real-world clinical and research workflows.