# Moving Healthcare AI Support Systems for Visually Detectable Diseases to Constrained Devices
## 1 Introduction  
AI assistants are instrumental and important to the healthcare domain. However, AI assistants often require an Internet connection to function, which limits their use cases. This is an issue, as a United Nations (UN) report found that an estimated 37% of the world's population has never used the Internet [1]. Furthermore, data suggest that some countries in Asia and Africa have significant numbers of people not connected to the Internet [2]. Being unable to connect to the Internet, or having intermittent access, means being unable to access data from the cloud. Constrained devices (also known as 'low-resource' or 'edge' devices) are designed to work in such an environment. Smartphones can be used as constrained devices; however, they are still not globally accessible, contrary to popular belief. For example, Sudan's smartphone ownership per capita is only 0.197 [3]. This presents an exciting opportunity to use low-power and low-cost devices, such as Raspberry Pis, to make healthcare AI support systems accessible to those in low-connectivity areas. Raspberry Pis have already been used to democratise healthcare AI in Africa in a 2024 study by [PERSON] et al. [4]. Our work builds on this by focusing on the diagnosis of visually detectable diseases (VDDs). The ability to deploy healthcare AI support systems on constrained devices could enable their distribution in remote areas and extreme environments such as rural areas, caves, mines, and even space--contributing to the democratisation of healthcare.  
One way to achieve this is through tinyML, which uses small devices such as microcontrollers to deploy machine learning (ML) algorithms. In recent years, there have been many advancements in ML, computer vision, and image classification for use in the medical domain. Deep learning, particularly the use of convolutional neural networks (CNNs), has become state-of-the-art in automated visual understanding [5] due to its efficient performance in regression and identification tasks [6]. As stated by [PERSON] et al. [7], deep learning shows promise for medical image analysis, which could in turn improve patient outcomes. This is imperative, as the rapid increase in the number of medical images produced in recent years means medical professionals are struggling to keep up with the workload [7]. Developing AI systems that can automate arduous and time-consuming processes such as diagnosing VDDs could help to solve this problem.  
To address this problem, we build on the recent advances in image classification and computer vision with the following contributions:  
1. We designed a novel tinyML prototype based on device capacity and state-of-the-art image classification approaches that can be used to identify skin diseases by pointing the device camera at the patient.
2. We introduced a novel application of image classification for low-resource situations.
3. We conducted an experimental evaluation and comparative assessment of our approach against existing image classification models.  
The rest of the paper is structured as follows: Section 2 reviews related work. Section 3 discusses ethical considerations, and Section 4 describes our proposed system architecture and implementation. Section 5 provides a discussion of the results and comparative assessment, and finally, Section 6 concludes our work.