PUBLICATION IN ACADEMIC PRESS, ELSEVIER

SCOPE:

 The demand for  "AI-Driven Diagnostics and Disease Prediction: Optimizing Deep Learning for Healthcare" is made up of a varied range of healthcare professionals and educators. Healthcare administrators and IT professionals are interested in implementing strategic AI to improve patient care and operational efficiency. Biomedical engineers and researchers in the healthcare business hope to use AI to provide novel solutions. While regulatory professionals in the healthcare business focus on maintaining ethical and regulatory compliance, academic institutions can utilize the book as a beneficial textbook in healthcare informatics and associated programs. Furthermore, students and data scientists interested in healthcare data analysis will find the book useful for academic study and project direction, and professional development programs will utilize it to teach healthcare professionals. To achieve these objectives, both theoretical advances and their applications to real-life problems will be stressed. This has been done to make the edited book more flexible and to stimulate further research interest in topics. It is expected that all academic institutions and universities will procure the volume as a reference book. This comprehensive secondary audience encompasses professionals, educators, researchers, and students across various sectors, all seeking valuable insights into the application of AI in healthcare with a focus on diagnostics and disease prediction, as well as the optimization of deep learning techniques in this context.

 This book is aimed at healthcare professionals, such as physicians, nurses, radiologists, and administrators, who want to use AI and deep learning to enhance diagnoses and disease prediction in the clinical environment. It provides information on how artificial intelligence might improve patient care and expedite healthcare processes.  This book is vital for data scientists, machine learning engineers, and academics who want to understand how to use and optimize deep learning techniques in healthcare. It provides useful advice on model creation, data preparation, and ethical issues for healthcare applications. The major audience group represents the book's primary beneficiaries and is critical to the effective adoption of AI-driven diagnostics and illness prediction in the healthcare sector.  

The book has the potential to serve as a resource for academic courses in healthcare informatics, data science, and biomedical engineering, offering learners and teachers both basic information and concrete recommendations. It also serves people from other industries who are interested in implementing and optimizing AI for healthcare applications.

TOPICS (PROPOSED TENTATIVE CHAPTERS)

• Introduction to AI in Healthcare using Machine Learning and Deep Learning

• The Importance of Diagnostics and Disease Prediction for Real World Data Sets.

• Neural Networks and Deep Learning Frameworks

• Deep Learning Architectures for Healthcare and Types of Healthcare Data, Data Collection and Sources

• Data Pre-processing and Cleaning, Handling Data Privacy and Security

• Building Machine Learning Models: Supervised Learning for Diagnostics and Unsupervised Learning for Disease Prediction

• Building Deep Learning Models: Convolutional Neural Networks (CNNs) for image analysis from Healthcare Sectors

• Recurrent Neural Networks (RNNs) for time-series data Transfer learning and pretrained models

• Natural Language Processing for Healthcare Texts

• Predictive Modeling for Early Disease Detection

• Telemedicine and Remote Diagnostics

• Ensuring Patient Privacy, Informed Consent, Ethical and Regulatory Considerations

• Future Trends and Innovations in Healthcare AI, Quantum Computing, and Edge Computing

• Multimodal Data Fusion for Enhanced Diagnostics