Machine Learning for Healthcare Applications

Abstract

Machine learning (ML) is revolutionizing healthcare by offering innovative solutions that enhance delivery, outcomes, and efficiency. This chapter highlights ML's significance in healthcare, addressing current trends, challenges, and techniques like supervised, unsupervised, and reinforcement learning. Applications in disease diagnosis, medical imaging, drug discovery, personalized treatments, and remote monitoring are explored. It also delves into data sharing, security, and real-time monitoring through IoT and blockchain, along with NLP advancements automating clinical documentation and sentiment analysis. Real-life case studies on AI-based radiology and chronic disease prediction demonstrate its impact. Ethical and regulatory issues, such as data privacy, biases in AI models, and emerging paradigms like federated learning for secure, decentralized healthcare, are discussed. The chapter envisions a future where ML transforms healthcare to become more efficient, personalized, and accessible while addressing critical ethical considerations.

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