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Modern Advancements in AI for Healthcare Diagnostics
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2026
Jahr
Abstract
This article provides a comprehensive overview of AI's transformative role in modern healthcare diagnostics. It meticulously charts the historical evolution from early expert systems like MYCIN to the current landscape dominated by deep learning and natural language processing. It also delves into core concepts, methodologies such as machine learning and Convolutional Neural Networks (CNNs), and architectural designs including cloud, edge, and Internet of Things (IoT) frameworks. A significant portion is dedicated to critical ethical considerations such as data privacy (including HIPAA and GDPR), algorithmic bias, the necessity for explainable AI (XAI), and evolving regulatory frameworks like FDA guidelines and EU AI Act. Practical applications in radiology, pathology, and genomics are showcased, alongside persistent challenges including data quality, system integration, and clinician trust. Finally, the article explores future innovations like wearable technology, personalized analytics, and agentic AI, positioning AI as a force for a more predictive and equitable diagnostic era.
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