# Synthetic Medical Imaging Generation with Generative Adversarial Networks for Plain Radiographs
## 1 Introduction  
The purpose of this investigation was to address data shortages and patient privacy concerns in the medical imaging space by generating synthetic imaging data, specifically knee and elbow radiographs. Ultimately, this work provides groundwork towards a standardized methodology for generating clinically realistic synthetic medical images on a large scale in order to increase access to data and improve clinical algorithms. Though there are recognized benefits of artificial intelligence (AI) algorithms in the healthcare space, they require robust amounts of training data in order to be useful. Access to these data can be hindered by patient privacy protections [1]. Furthermore, the rarity of certain pathologies of interest also adds to data scarcity [1]. The combination of the aforementioned limitations greatly decreases the participation of smaller entities and independent researchers in the development of healthcare AI compared to larger institutions and/or medical systems who have data access advantages. These hindrances create time delays and are prohibitive to rapid development across the domain of clinical AI. Data access can be streamlined through partnerships between developers and specific data providers, such as a hospital and/or medical system. However, the available data are often subpopulation-specific. This results in siloed algorithm development where the algorithms are only applicable to a small population and can be difficult to scale for universal purposes. Furthermore, this creates difficulties comparing and regulatingalgorithms, particularly across geographic entities and different healthcare systems. These hurdles in access to data lead to delays to project timelines and limitations to innovation relating to these data. Given the substantial cost and time barriers to access, the innovation in clinical AI has lagged compared to other fields. The majority of research in general AI has been targeted toward creating larger and more complex models. However, with this increase in model size, access to larger amounts of data becomes more important than model architecture [2]. Additionally, when the size of the data used is large enough, simpler models have comparable performance to robust architectures [3]. This highlights a significant need for large amounts of training data. In fact, current predictions indicate that synthetic data will make up most data used for training models by 2030 [4]. This prediction illustrates the case for processes to easily supplement AI practitioners with useful synthetic data, as will be provided with the proposed pipeline. In addition to a general need for synthetic data, there is also an increasing need for standardizing the evaluation of healthcare-related AI models. There is currently no agreed-upon standard evaluation metric for generative models [5]. The current pipeline supports these challenges by offering a standardized method for generating large datasets of synthetic medical images. In turn, this provides a pathway toward \"gold standard datasets\" which can be used to compare performance between algorithms developed on disparate populations, as well as provide insights to shape the regulatory process of AI models for medical imaging. To remedy the lack of publicly available medical images, researchers have experimented with various frameworks for generating medical images over the years. According to a 2020 study, the most popular frameworks for generating synthetic medical data have been autoencoders, U-nets, and Generative Adversarial Networks (GANs) [6]. Recently U-nets and GANs have become the most common approaches to this task, with the popularity of GANs outpacing that of U-nets [6]. In addition to the availability of data, there is emerging research in creating GAN architectures designed for more complex imaging modalities, such as Computed Tomography (CT) scans [7].