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Keywords = large vision models (LVM)

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29 pages, 6518 KB  
Article
Generative AI Models (2018–2024): Advancements and Applications in Kidney Care
by Fnu Neha, Deepshikha Bhati and Deepak Kumar Shukla
BioMedInformatics 2025, 5(2), 18; https://doi.org/10.3390/biomedinformatics5020018 - 3 Apr 2025
Cited by 5 | Viewed by 4236
Abstract
Kidney disease poses a significant global health challenge, affecting millions and straining healthcare systems due to limited nephrology resources. This paper examines the transformative potential of Generative AI (GenAI), Large Language Models (LLMs), and Large Vision Models (LVMs) in addressing critical challenges in [...] Read more.
Kidney disease poses a significant global health challenge, affecting millions and straining healthcare systems due to limited nephrology resources. This paper examines the transformative potential of Generative AI (GenAI), Large Language Models (LLMs), and Large Vision Models (LVMs) in addressing critical challenges in kidney care. GenAI supports research and early interventions through the generation of synthetic medical data. LLMs enhance clinical decision-making by analyzing medical texts and electronic health records, while LVMs improve diagnostic accuracy through advanced medical image analysis. Together, these technologies show promise for advancing patient education, risk stratification, disease diagnosis, and personalized treatment strategies. This paper highlights key advancements in GenAI, LLMs, and LVMs from 2018 to 2024, focusing on their applications in kidney care and presenting common use cases. It also discusses their limitations, including knowledge cutoffs, hallucinations, contextual understanding challenges, data representation biases, computational demands, and ethical concerns. By providing a comprehensive analysis, this paper outlines a roadmap for integrating these AI advancements into nephrology, emphasizing the need for further research and real-world validation to fully realize their transformative potential. Full article
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27 pages, 2910 KB  
Review
A Survey on Multimodal Large Language Models in Radiology for Report Generation and Visual Question Answering
by Ziruo Yi, Ting Xiao and Mark V. Albert
Information 2025, 16(2), 136; https://doi.org/10.3390/info16020136 - 12 Feb 2025
Cited by 4 | Viewed by 12093
Abstract
Large language models (LLMs) and large vision models (LVMs) have driven significant advancements in natural language processing (NLP) and computer vision (CV), establishing a foundation for multimodal large language models (MLLMs) to integrate diverse data types in real-world applications. This survey explores the [...] Read more.
Large language models (LLMs) and large vision models (LVMs) have driven significant advancements in natural language processing (NLP) and computer vision (CV), establishing a foundation for multimodal large language models (MLLMs) to integrate diverse data types in real-world applications. This survey explores the evolution of MLLMs in radiology, focusing on radiology report generation (RRG) and radiology visual question answering (RVQA), where MLLMs leverage the combined capabilities of LLMs and LVMs to improve clinical efficiency. We begin by tracing the history of radiology and the development of MLLMs, followed by an overview of MLLM applications in RRG and RVQA, detailing core datasets, evaluation metrics, and leading MLLMs that demonstrate their potential in generating radiology reports and answering image-based questions. We then discuss the challenges MLLMs face in radiology, including dataset scarcity, data privacy and security, and issues within MLLMs such as bias, toxicity, hallucinations, catastrophic forgetting, and limitations in traditional evaluation metrics. Finally, this paper proposes future research directions to address these challenges, aiming to help AI researchers and radiologists overcome these obstacles and advance the study of MLLMs in radiology. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU))
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