This review explores the innovative application of Artificial Intelligence (AI) in advancing camel health and welfare. It investigates the utilisation of various AI methodologies, including supervised learning, unsupervised learning, reinforcement learning, and Deep Learning techniques such as Convolutional Neural Networks (CNNs), specifically tailored towards the healthcare management of camels. The review highlights significant advancements in AI for early disease detection, diagnosis, treatment, and monitoring in camels, showcasing its pivotal role in precision medicine, automated disease diagnosis, and the optimization of treatment protocols. Notably, AI is effective in evaluating the toxicological impact of chemical substances, enhancing diagnostic accuracy through image-based diagnostics, and facilitating the early prediction of diseases like Trypanosoma evansi using artificial neural networks. Furthermore, AI contributes to the development of camel-derived diagnostic and therapeutic products, emphasising the utility of Machine Learning in analysing complex datasets for antibodies and nanobodies discovery and optimisation. Additionally, the application of AI in camel management and welfare, including weight prediction and the assessment of camel milk adulteration, illustrates the technology’s broader implications. The findings indicate that AI not only significantly enhances the accuracy and efficiency of camel healthcare and welfare but also opens new avenues for research and development in the domain of camelids. The study calls for further AI applications to fully harness AI’s potential in revolutionising camel healthcare and welfare practices, especially in the tracks of camel diseases treatment and control.
Key words: Artificial intelligence, camel, diagnosis, healthcare, machine learning
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