A Review of the Development and Applications of AI Large Models

Abstract

The rapid advancement of AI large models has significantly impacted various fields, including natural language processing, computer vision, multimodal applications, and medical imaging. This paper provides a comprehensive review of the development and applications of AI large models in these domains. By analyzing recent research, this paper highlights the technological breakthroughs enabled by AI large models and discusses the challenges and future directions.

1. Introduction

AI large models, characterized by their vast number of parameters and complex architectures, have become a driving force in the field of artificial intelligence. These models leverage large-scale data and advanced computational techniques to achieve remarkable performance in various tasks. This paper aims to provide an overview of the development and applications of AI large models in natural language processing, computer vision, multimodal applications, and medical imaging.

2. Applications in Natural Language Processing

Natural language processing (NLP) has seen significant advancements with the advent of AI large models. These models have demonstrated exceptional capabilities in understanding and generating human-like text.

2.1 Transformer Language Models

Transformer-based models have revolutionized NLP tasks. Large language models (LLMs) such as GPT have shown the ability to perform a wide range of language tasks, including text generation, translation, summarization, sentiment analysis, and question answering. These models are trained on massive datasets and leverage advanced attention mechanisms to capture intricate language patterns.

2.2 Notable Models

• GPT Series: Developed by OpenAI, the GPT series has evolved from GPT-1 to GPT-4, with each iteration significantly increasing in parameter size and capabilities. GPT-4 introduced multimodal capabilities, allowing it to understand images in addition to text.
• LLaMA Series: Introduced by Meta AI, the LLaMA series includes models with varying parameter sizes, demonstrating strong performance across multiple NLP tasks.

3. Applications in Computer Vision

Computer vision has also benefited from the development of AI large models, particularly in tasks such as image classification, segmentation, and generation.

3.1 Vision Transformer (ViT)

ViT has shown competitive performance compared to traditional convolutional neural networks (CNNs) by leveraging the Transformer architecture for image processing. This model divides images into patches and processes them similarly to tokens in text, capturing spatial and contextual information effectively.

3.2 MAE and VideoMAE

MAE, introduced by a team led by He Kaiming, applies self-supervised learning to ViT, achieving impressive results with significantly less training data. VideoMAE extends this concept to video data, demonstrating efficient pre-training for video analysis tasks.

4. Applications in Multimodal Applications

Multimodal AI large models integrate multiple types of data, such as text and images, to perform tasks that require understanding and generating content across modalities.

4.1 CogVLM

Developed by Tsinghua University and Zhiyuan AI, CogVLM integrates visual and language features, achieving top performance in multimodal benchmarks. This model demonstrates the potential of large models in tasks that require both visual and textual understanding.

4.2 LLaVA

LLaVA is a multimodal large language model that connects visual encoders with language models to perform generic visual and language understanding tasks. This model showcases the ability of large models to handle complex multimodal interactions.

5. Applications in Medical Imaging

Medical imaging has seen significant advancements with the application of AI large models, particularly in tasks such as diagnosis, segmentation, and image generation.

5.1 CheXzero and ChatCAD

CheXzero, a zero-shot chest X-ray classifier, has demonstrated radiologist-level performance in classifying multiple pathologies. ChatCAD integrates multiple diagnostic networks with ChatGPT, offering a potential application of LLMs in computer-aided diagnosis (CAD) for medical images.

5.2 MedCLIP and PLIP

MedCLIP is a contrastive learning framework that demonstrates impressive zero-shot medical image classification accuracy, particularly in detecting Covid-19 infections. PLIP, built using image-text pairs from medical Twitter, enables both image-based and text-based pathology image retrieval.

5.3 3D Medical LVMs

Developing 3D medical large vision models (LVMs) is crucial for handling 3D medical imaging modalities such as CT and MRI. Med3D, a heterogeneous 3D framework, shows strong generalization capabilities in downstream tasks like lung segmentation and pulmonary nodule classification.

6. Challenges and Future Directions

Despite the significant progress, AI large models face several challenges. The training and deployment of these models require substantial computational resources. Additionally, the interpretability of these models is often limited, which is a critical concern in fields like healthcare. Future research should focus on optimizing model architectures, improving interpretability, and addressing data privacy and security concerns.

7. Conclusion

AI large models have demonstrated remarkable capabilities in various fields, including natural language processing, computer vision, multimodal applications, and medical imaging. These models have enabled technological breakthroughs and have the potential to transform multiple industries. However, addressing the current challenges will be crucial for the continued development and application of AI large models.