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Integrating Large Language Models for Advanced Imaging Applications

Integrating Large Language Models for Advanced Imaging Applications
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Author(s): Samyukta Rongala (University of Missouri, Saint Louis, USA)and Teja Krishna Kota (New England College, USA)
Copyright: 2026
Pages: 30
Source title: Radiodiagnosis in the Era of AI
Source Author(s)/Editor(s): Praveen Kumar (Datta Meghe Institute of Higher Education and Research, Wardha, India), Prateek Verma (Dayananda Sagar University, Bangalore, India), Gaurav Vedprakash Mishra (Datta Meghe Institute of Higher Education and Research, Wardha, India), Gopal Singh Phartiyal (University of Leeds, UK)and Anurag Ashok Luharia (Datta Meghe Institute of Higher Education and Research, Wardha, India)
DOI: 10.4018/979-8-3373-0903-3.ch007

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Abstract

This chapter explores the transformative potential of large language models (LLMs) in conjunction with image technology, highlighting their ability to redefine limitations in language production and comprehension. By examining their integration into various fields, including medical imaging, autonomous systems, and multimedia content analysis, the chapter underscores the potential for revolutionizing these domains. The discussion also considers the role of LLMs and artificial intelligence in regulatory technology (RegTech) solutions, aimed at streamlining policy processes and mitigating risks. A detailed overview of current studies evaluates the effectiveness of AI and machine learning (ML) combinations. The chapter presents a methodology wherein an AI-based model, utilizing LLMs, analyzes data from diverse processes and datasets. This approach reveals the advantages of AI applications in corporate finance, particularly in enabling financial specialists to derive meaningful insights from extensive datasets.

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