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SOURCE: Zamanifar A. Application of Generative AI in Healthcare Systems 2025
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MEDIAINFO
Textbook in PDF format Generative AI has immensely influenced various fields, such as education, marketing, art and music, and especially healthcare. Generative AI can benefit the patient through various approaches. For instance, it can enhance the image qualities negatively affected by radiation reduction, preventing patients from needing to repeat the image-taking process. Also, the generation of one type of image from another more expensive one can help patients save funds. Generative AI facilitates the administrative process, letting the doctor focus more on the treatment process. It even goes further by helping medical professionals with diagnosis and decision-making, suggesting possible treatment plans according to the patient symptoms. This book introduces several practical GenAI healthcare applications, especially in medical imaging, pandemic prediction, synthetic data generation, clinical administration support, professional education, patient engagement, and clinical decision support, providing a review of efficient GenAI tools and frameworks in this area. GenAI empowers the treatment process through several methods; however, some ethical, privacy, and security challenges require attention. Despite the challenges presented, GenAI technological and inherited characteristics smooth the path of improvement for it in the future. As a primary approach, language modeling has been extensively studied over the past two decades to understand and generate language, evolving from statistical language models to neural language models. Large language models (LLMs) are gaining increasing popularity in academia and industry due to their unprecedented performance across various applications. Recent research on LLMs by academia and industry has advanced significantly, with a notable development being the launch of Chat-GPT, which has garnered widespread attention from the community. Language models can appear remarkably intelligent. This is especially true in extracting correct answers from documents, as they excel in this process due to their mastery of human language and the precise settings they have received in question-and-answer datasets. This process resembles a human agent reading documents to extract information but operates at a much faster rate than a human agent. Large language models leverage numerous elements and processes for their operation. For instance, deep neural networks are a core element in the structure of large language models. Python tools have become increasingly important for implementing generative models. Here, we focus on key Python-based libraries and frameworks designed to utilize generative models specifically for medical imaging. These tools are essential for integrating GenAI into medical workflows, where the challenges of data scarcity, multimodality, and high dimensionality make traditional approaches less sufficient. Medical Open Network for AI (MONAI) is a Deep Learning library built explicitly for medical imaging tasks and provides flexible tools for training models that can enhance, reconstruct, or generate medical images. TorchIO is built on PyTorch to work with 3D medical images, making it ideal for volumetric datasets such as MRI or CT scans. While it is primarily used for preprocessing and augmentation, it is highly complementary to GenAI workflows. Medigan is an open-source Python library specifically designed for generative models in medical imaging. Its architecture is built to standardize the integration of generative models and facilitate reproducibility in synthetic image generation, especially in healthcare contexts. Medigan encapsulates several pre-trained generative models and provides an easy interface for training, evaluating, and fine-tuning them on medical datasets. Pythae is a Python library designed to work with autoencoders, particularly variational autoencoders (VAEs) and their variants. Although Pythae is not exclusively built for medical imaging, its flexible and modular design makes it highly relevant in this domain, where VAEs are utilized for tasks such as image reconstruction, anomaly detection, and data compression
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