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Overview: This book presents state-of-the-art research works for a better understanding of the advantages and limitations of AI techniques in the field of healthcare. It will further discuss Artificial Intelligence applications in depression, hypertension and diabetes management. The text also presents an Artificial Intelligence chatbot for depression, diabetes, and hypertension self-help. Many researchers have acknowledged Artificial Intelligence (AI) and Digital Twins (DT) as crucial technologies for the upcoming decade. They can optimise and integrate modern technologies like analytics, Artificial Intelligence and the Internet of Things (IoT). AI could revolutionize healthcare by improving efficiency, accuracy, and patient outcomes. Some of the notable healthcare applications of AI and DT in the domains of diagnostic imaging, such as radiology and pathology, could help radiologists and pathologists understand X-rays, MRIs, and CT images. AI could improve picture analysis in these sectors by discovering complicated patterns and abnormalities that challenge human visual perception. AI analyses large databases to speed up drug discovery. This technique finds new medication candidates, predicts their efficacy, and optimises their chemical structures. Personalised medicine uses AI to analyse patient data, including genetic information, to create treatment plans that match an individual’s qualities. This optimises medicine selection and dosing. Artificial Intelligence–powered virtual health assistants may answer questions and book appointments. The subtypes of AI known as Machine Learning (ML) and Deep Learning (DL) are both capable of finding creative solutions to challenges. Although ML research in precision cardiovascular care has expanded recently, Deep Learning is more recent, more sophisticated, and has different advantages and limits than ML. ML is useful for prediction by examining mechanisms and their correlations with specified variables using different training datasets, which may include different varieties and important data, such as multi-omics, social media, wearable technology, and standardized electronic health records. Both supervised and unsupervised learning are used for machine ML.
Genre: Non-Fiction > Tech & Devices
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