# Moving Healthcare AI Support Systems for Visually Detectable Diseases to Constrained Devices
## 2 Related Work
### TinyML  
Being unable to connect to the Internet, or having intermittent access, means being unable to access data from the cloud. TinyML and edge computing could solve this issue, as hosting AI assistants on constrained devices can eliminate connectivity issues by processing data within the device itself, without the need for Internet or cloud access [15]. However, as AI applications such as healthcare AI support systems are memory-intensive and require data to be processed quickly, it is important to examine the capability of the constrained device(s) chosen to deploy them. Table 1 shows a variety of easily available devices that could be used to host a healthcare AI assistant. From this table, the most well-known and widely used device is the Raspberry Pi. It is also the most affordable option, costing approximately GBP 20-40 at the time of writing. However, devices such as the UDOO BOLT and Orange Pi 5 are advantageous as their larger memory and fast processor speeds make them more suitable for AI and image processing applications. Additionally, the Nvidia Jetson Nano, Orange Pi 5, and UDOO BOLT come with GPU acceleration, which is crucial for running real-time inference. However, these benefits come at a price, with the three aforementioned devices costing at least three times more than the Raspberry Pi. [PERSON] et al. (2016) and [PERSON] et al. (2017) conducted research into using a cluster of Raspberry Pis to host serverless platforms. [PERSON]'s work found that it was possible, but that there were shortcomings in performance compared to standard PC hardware. However, as this research was published in 2017, Raspberry Pi 2 models were used; this may have contributed to the performance issues found. Despite this, a positive outcome of their research was that they concluded future generations of devices (in this case, the Raspberry Pi 3 model and beyond) would make successfully hosting serverless platforms more viable, especially for use cases in harsh environments. Cost is another benefit of using low-resource devices instead of expensive cloud servers. This is supported by [PERSON] et al. (2018), who stated that one of the reasons edge computing has attracted so much interest over recent years is because of its low communication costs. These communication costs are reduced owing to the ability of many edge nodes to preprocess large amounts of data before they are sent to the cloud. Additionally, we note that research has been conducted using smartphones for tinyML. However, smartphones are costly and not widely available or widely used by people in developing countries (where access to the Internet is also limited) ([PERSON] et al., 2018). Due to this, we chose not to deploy our system onto a smartphone in our study. In recent years, there have been developments in using tinyML for the diagnosis of skin conditions. [PERSON] et al. (2020) developed 'Imitdad', an AI architecture that provides skin condition diagnosis on cloud, fog, and edge platforms. They deployed their architecture on many platforms, including a local smartphone, a laptop, an NVIDIA Jetson Nano, and a Raspberry Pi 4 Model B (RPi 4), with a focus on energy efficiency and speed. Their work was wide-ranging, with their system providing 22 skin condition diagnosis services. However, this work was focused on the impact of developing distributed AI healthcare services, rather than focusing on the accuracy of diagnoses. Users access the system via a mobile app, take or upload a skin image, and select a specific classification service. The image is then processed by a deep learning model, including one of two tinyML models designed to run on constrained devices (a pre-trained adaptation of Inception V3 ([[https://keras.io/api/applications/inceptionv3/](https://keras.io/api/applications/inceptionv3/)]([https://keras.io/api/applications/inceptionv3/](https://keras.io/api/applications/inceptionv3/)), accessed on 20 June 2023) and a 'pure' CNN) and results are presented to the user via text on the user interface.