دانلود کتاب راهکارهای مراقبت‌های بهداشتی مبتنی بر اینترنت اشیا و هوش مصنوعی برای پیش‌بینی هوشمند بیماری‌ها

The rapid pace of technological development has transformed healthcare into a more innovative, proactive, and tailored space. The two main innovations that are revolutionizing the way we expect and treat disease in our digital age are the Internet of Things (IoT) and Artificial Intelligence (AI). Building new healthcare systems—this section explores how these new technologies work together to build new societies and better outcomes for prediction of disease and general health. Wearable sensors, smart medical devices, and remote monitoring tools continuously collect real-time health data, providing vital insights about the health status of individuals. These devices, when used in combination with AI-powered data analytics, can detect early indications of disease and alleviate pressure on public health and help refocus on preventive healthcare. The AI algorithms apply machine learning (ML) and deep learning (DL) techniques on aggregated medical data to identify trends and predict disease progression.
This part will elaborate the IoT and AI-driven disease early warning system basics by looking big data analytics, cloud computing, edge AI in healthcare systems. The document highlights that as AI models such as decision trees, neural networks and Natural language processing (NLP) enhance forecasting accuracy and speed of diseases. For example predictive chronic diseases like diabetes and heart problems to Alzheimer’s and Parkinson’s disease centered neurological conditions being discovered early on. Examinations are on the line of AI in terms of detection and diagnosis in cancer, outbreak analysis for infectious disease and personalized medicine. However, there are still some hurdles that are yet to be cleared. Within this framework, there are security data and privacy violations with respect to AI based diagnosis as well as the requirement to work on IoT frameworks compatibility together with standardization among the AI model. Second, there are general research questions that we must confront such as biases of AI models and explainable artificial intelligence (XAI).
The future of AI-enabled healthcare appears promising, as technologies like 5G, blockchain, and federated learning facilitate real-time AI analytics. These advancements can provide more sophisticated, secure, and readily deployable healthcare solutions. As they mature, these technologies will be key for personalising preventive and predictive medicine and step by health outcomes globally.
This is a worthwhile book for readers who want to explore the relationship between the Internet of Things (IoT), artificial intelligence (AI), and disease prediction. It should appeal to researchers, clinical medical professionals, AI developers, and anyone who has an interest in diving into these fields. This study strives to invest in transformation of healthcare aided by smart and data driven innovations by shedding light over the recent trends, applications and bottlenecks.