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 next-generation healthcare services are sprinting 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 will 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 are postgraduate students and researchers in the broad domain of healthcare technologies.
Features
- In-depth coverage of the role of AI in smart healthcare
- Research guidelines for AI and data science researchers/practitioners interested in the healthcare sector
- Comprehensive coverage on security and privacy issues for AI in smart healthcare
TABLE OF CONTENTS
1. Introduction.
2. Machine Learning for Disease Assessment.
3. Precision Medicine and Future Healthcare.
4. AI-driven Drug Response Prediction for Personalized Cancer Medicine.
5. Skin Disease Recognition and Classification Using Machine Learning and Deep Learning in Python.
6. COVID-19 Diagnosis Based Deep Learning Approaches for COVIDX Dataset: A Preliminary Survey.
7. Automatic Grading of Invasive Breast Cancer Patients for the Decision of Therapeutic Plan.
8. Prognostic Role of CALD1 in Brain Cancer: A Data-driven Review.
9. Artificial Intelligence for Parkinson's Disease Diagnosis: A Review.
10: Breast Cancer Detection: A Survey.
11. Review of Artifact Detection Methods for Automated Analysis and Diagnosis in Digital Pathology.
12. Machine Learning Enabled Detection and Management of Diabetes Mellitus.
13. IoT and Deep Learning-based Smart Healthcare with an Integrated Security System to Detect Various Skin Lesions.
14. Real-Time Facemask Detection Using Deep Convolutional Neural Network-based Transfer Learning.
15. Security Challenges in Wireless Body Area Networks for Smart Healthcare.
16. Machine Learning Based Security and Privacy Protection Approach to Handle the Physiological Data.
17. Conclusion: Future Challenges in Artificial Intelligence for Smart Healthcare.
