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The book stands at the intersection of medical virology, computer science engineering, and artificial intelligence. This interdisciplinary book seeks to harness the strengths of these fields to address the complex challenges of diagnosing, treating, and managing viral diseases. Medical virology, which studies viruses and virus-like agents, plays a critical role in understanding infectious diseases and developing therapeutic strategies. However, the field faces immense challenges due to the sheer volume and complexity of virological data, which demands advanced computational tools for effective analysis and application.
Computer science engineering provides the technological backbone for this initiative. Engineers in this field develop and implement sophisticated algorithms and data structures that enable the processing of large-scale virological datasets. In this context, computer science engineering is pivotal for creating the infrastructure necessary for deep learning and blockchain technologies. Deep learning, a branch of artificial intelligence, involves training neural networks to recognize patterns in vast datasets, enabling the discovery of insights that are otherwise obscured. When applied to medical virology, deep learning can significantly enhance the accuracy of viral diagnostics, predict disease outbreaks, and personalize treatment regimens based on the genetic makeup of both viruses and patients
Sommario
Part 1. Fundamentals of AI and Deep Learning in Healthcare.- Chapter 1. Artificial Intelligence and Deep Learning in Healthcare.- Chapter 2. Human-Centric Artificial Intelligence (HCAI) for Precision Clinical and Medical Virology.- Chapter 3. Deep Learning Algorithms and Techniques.- Chapter 4. Applications of Deep Learning in Virology.- Part 2. AI and Blockchain Applications in Medical Virology.- Chapter 5. Deep Learning in Viral Diagnosis: Case Studies and Emerging Frameworks for Precision Medical Virology.- Chapter 6. Implementing Machine Learning for Analyzing Influenza A Virus with Hemagglutinin Sequences.- Chapter 7. Transforming Global Health: The Impact of AI and Blockchain on Viral Disease Control.- Chapter 8. A blockchain-based trust architecture for securing pandemic test results in decentralized health networks.- Chapter 9. Building Trust through AI: AI Approaches to Medical Virology in Future Healthcare Systems.- Chapter 10. Integrating Federated Deep Learning and Blockchain for Privacy-Preserving Precision Medicine in Medical Virology.- Chapter 11. Integrating Deep Convolutional Neural Network with Blockchain for Secure and Transparent Data Management in Decentralized Healthcare Systems.- Chapter 12. Case Studies: Blockchain in Medical Virology.- Part 3. Advanced AI for Disease Prediction and Medical Innovations.- Chapter 13. Bio-Inspired Approaches for Optimal Kidney Paired Donation (Infectious Risk Analysis).- Chapter 14. Anemia Prediction Based on Eye Condition Data.- Chapter 15. AI for Multi-Region Tumour Detection: Enhancing Human Workflow in Full-Body Scans.- Chapter 16. Enhancing classification accuracy in virology through deep learning for accurate virus identification from tem imagery.- Chapter 17. Advancing prognostic insights: a novel deep learning algorithm to predict outcomes in amyotrophic lateral sclerosis.- Chapter 18. Cultivating Innovative AI Paradigms for Enriching Patient Engagement and Elevating Telehealth Services.- Chapter 19. Ethical and legal considerations in leveraging ai and blockchain for equitable healthcare access for disabled populations.- Chapter 20. Analysing the Impact & Challenges of Deep Learning Models in Virology.
