Artificial Intelligence (AI) in general and machine learning (ML) and deep learning (DL) in particular and related digital technologies are a couple of fledging paradigms that the next generation healthcare services are sprouting towards. These digital technologies can transform various aspects of healthcare, leveraging advances in computing and communication power. With a new spectrum of business opportunities, AI-powered healthcare services would improve the lives of patients, their families, and societies. However, the application of AI in the healthcare field requires special attention given the direct implication with human life and well-being. Rapid progress in AI leads to the possibility of exploiting healthcare data for designing practical tools for automated diagnosis of chronic diseases such as dementia and diabetes. This book highlights the current research trends in applying AI models in various disease diagnoses and prognoses to provide enhanced healthcare solutions. The primary audience of the book will be postgraduate students and researchers in the broad domain of healthcare technologies.
Features
In-depth coverage of the role of AI in smart healthcare.
Research guideline for AI and data science researchers/practitioners interested in the healthcare sector.
Comprehensive coverage on security and privacy issues for AI in smart healthcare.
Author(s): Ghita Kouadri Mostefaoui, S. M. Riazul Islam, Faisal Tariq
Publisher: CRC Press
Year: 2023
Language: English
Pages: 327
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Editors
Contributors
Chapter 1: Introduction to Artificial Intelligence (AI) for Disease Diagnosis and Prognosis in Smart Healthcare
Chapter 2: Machine Learning for Disease Assessment
Chapter 3: Precision Medicine and Future Healthcare
Chapter 4: AI-Driven Drug Response Prediction for Personalized Cancer Medicine
Chapter 5: Skin Disease Recognition and Classification Using Machine Learning and Deep Learning in Python
Chapter 6: COVID-19 Diagnosis-Based Deep Learning Approaches for COVIDx Dataset: A Preliminary Survey
Chapter 7: Automatic Grading of Invasive Breast Cancer Patients for the Decision of Therapeutic Plan
Chapter 8: Prognostic Role of CALD1 in Brain Cancer: A Data-Driven Review
Chapter 9: Artificial Intelligence for Parkinson’s Disease Diagnosis: A Review
Chapter 10: Breast Cancer Detection: A Survey
Chapter 11: Review of Artifact Detection Methods for Automated Analysis and Diagnosis in Digital Pathology
Chapter 12: Machine Learning-Enabled Detection and Management of Diabetes Mellitus
Chapter 13: IoT and Deep Learning-Based Smart Healthcare with an Integrated Security System to Detect Various Skin Lesions
Chapter 14: Real-Time Facemask Detection Using Deep Convolutional Neural Network-Based Transfer Learning
Chapter 15: Security Challenges in Wireless Body Area Networks for Smart Healthcare
Chapter 16: Machine Learning-Based Security and Privacy Protection Approach to Handle Physiological Data
Chapter 17: Future Challenges in Artificial Intelligence for Smart Healthcare
Index
