# Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis

arXiv: https://arxiv.org/abs/2403.17549 · HTML: https://mlsanity.com/paper/2403.17549/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 15 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Medical Image Synthesis with Context-Aware Generative Adversarial Networks](https://mlsanity.com/paper/1612.05362/) (2016)
- [Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging](https://mlsanity.com/paper/1806.05452/) (2018)
- [Baichuan2-Sum: Instruction Finetune Baichuan2-7B Model for Dialogue Summarization](https://mlsanity.com/paper/2401.15496/) (2024)
- [Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data](https://mlsanity.com/paper/2402.09830/) (2024)
- [Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models](https://mlsanity.com/paper/2402.12916/) (2024)
- [Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis](https://mlsanity.com/paper/2402.15994/) (2024)
- [Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving](https://mlsanity.com/paper/2402.16036/) (2024)
- [A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration](https://mlsanity.com/paper/2402.19095/) (2024)
- [Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations](https://mlsanity.com/paper/2402.16035/) (2024)
- [Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research](https://mlsanity.com/paper/2402.16038/) (2024)
- [Enhanced User Interaction in Operating Systems through Machine Learning Language Models](https://mlsanity.com/paper/2403.00806/) (2024)

## Abstract (arXiv)

The medical field is one of the important fields in the application of artificial intelligence technology. With the explosive growth and diversification of medical data, as well as the continuous improvement of medical needs and challenges, artificial intelligence technology is playing an increasingly important role in the medical field. Artificial intelligence technologies represented by computer vision, natural language processing, and machine learning have been widely penetrated into diverse scenarios such as medical imaging, health management, medical information, and drug research and development, and have become an important driving force for improving the level and quality of medical services.The article explores the transformative potential of generative AI in medical imaging, emphasizing its ability to generate syntheticACM-2 data, enhance images, aid in anomaly detection, and facilitate image-to-image translation. Despite challenges like model complexity, the applications of generative models in healthcare, including Med-PaLM 2 technology, show promising results. By addressing limitations in dataset size and diversity, these models contribute to more accurate diagnoses and improved patient outcomes. However, ethical considerations and collaboration among stakeholders are essential for responsible implementation. Through experiments leveraging GANs to augment brain tumor MRI datasets, the study demonstrates how generative AI can enhance image quality and diversity, ultimately advancing medical diagnostics and patient care.