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bathla g artificial intelligence healthcare 2025

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Textbook in PDF format 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. A dataset must be chosen considering different parameters like assumptions or an algorithm such as a neural network, a support vector machine, or a k-nearest neighbour algorithm. Deep Learning (DL) is used for pattern recognition in processes like the analysis of images, including input from imaging of cardiac computerized tomography. With both supervised and unsupervised techniques, DL can carry out automated predictive analysis, given the growing availability of a database of EHR, imaging of CV, and data from developing wearable devices. This book Provides a structured overview of recent developments of Artificial Intelligence applications in the healthcare sector. Presents an in-depth understanding of how Artificial Intelligence techniques can be applied to diabetes management. Showcases supervised learning techniques based on datasets for depression management. Discusses artificial intelligence chatbot for diabetes, depression, and hypertension self-care. Highlights the importance of Artificial Intelligence in managing and predicting diabetes, hypertension, and depression. Preface Artificial Intelligence and Digital Health Twin Applications in Healthcare—A Systematic Review Early Detection of Diabetic Foot Ulcers Using Optical Flow–Based Ensemble Learning CNN Framework Identification of Potential Biomarkers for Diabetes Machine Learning for Chronic Diseases Machine Learning for Chronic Diseases AI Chatbot for Diabetes Self-Help Future of Diabetic Management Using Artificial Intelligence Detection of Generalized Anxiety Disorder Machine Learning Techniques for Prediction of Hypertension Food Recommendations for Hypertensive Persons Optimized Support Vector Machines for Detection of Mental Disorders Artificial Intelligence Applications in Depression Management Depression Prediction Using Machine Learning Techniques Future of Depression Management Using Artificial Intelligence AI Chatbot for Depression Self-Help Artificial Intelligence in Healthcare: Futuristic Opportunities

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bathla g artificial intelligence healthcare 2025