Image classification usually requires connectivity and access to the cloud, which is often limited in many parts of the world, including hard-to-reach rural areas. Tiny machine learning (tinyML) aims to solve this problem by hosting artificial intelligence (AI) assistants on constrained devices, eliminating connectivity issues by processing data within the device itself, without Internet or cloud access. This study explores the use of tinyML to provide healthcare support with low-spec devices in low-connectivity environments, focusing on the diagnosis of skin diseases and the ethical use of AI assistants in a healthcare setting. To investigate this, images of skin lesions were used to train a model for classifying visually detectable diseases (VDDs). The model weights were then offloaded to a Raspberry Pi with a webcam attached, to be used for the classification of skin lesions without Internet access. It was found that the developed prototype achieved a test accuracy of 78% when trained on the HAM10000 dataset, and a test accuracy of 85% when trained on the ISIC 2020 Challenge dataset.

Machine translation (MT) usually requires connectivity and access to the cloud which is often limited in many parts of the world, including hard to reach rural areas. Natural language processing (NLP) on the edge aims to solve this problem by processing language data closer to the source. To achieve this, 100 sentence pairs were stored and processed on a Raspberry Pi, and a recurrent neural network (RNN) using the long short-term memory (LSTM) architecture was used for MT. We are focusing on translating between English and Hausa, a low-resource language spoken in West Africa. It was found that the developed prototype produced “good and fluent translations” with a training accuracy of 91%. The model also achieved a BLEU score of 73.5, compared to the existing models that have scores of 22.2 and below.