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Artificial intelligence and predictive technology are revolutionizing healthcare by enabling more accurate diagnoses, personalized treatment plans, and proactive patient care. They can forecast disease progression, predict patient readmission risks, identify individuals at high risk for conditions like sepsis or heart failure, and optimize treatment protocols based on individual patient characteristics. By shifting healthcare from a reactive to a predictive model, these technologies not only improve patient outcomes and reduce mortality rates but also significantly lower healthcare costs by preventing complications and reducing unnecessary procedures, ultimately creating a more efficient and effective healthcare ecosystem.
Artificial Intelligence for Predictive Healthcare: Towards Personalized Treatment and Disease Prevention delves into the algorithms, technologies, and applications that are driving this transformation of healthcare. Highlights include:
- Optimizing diagnosis and treatment plans with AI
- Machine learning and generative AI for cancer diagnosis and treatment
- The evolving role of healthcare professionals in smart healthcare
- Hybrid machine learning algorithms for early prediction of diabetes
Bringing together the perspectives of professionals, researchers, and practitioners working at the intersection of technology and healthcare, the book reflects a shared belief that AI's role in healthcare is not just about algorithms and data, but about improving lives. From predicting disease outbreaks to creating tailored treatment plans, the book covers a range of applications. With real-world examples and case studies, it offers a roadmap to understanding AI's potential to predict, personalize, and prevent health conditions.
List of contents
1. Introduction to AI in Healthcare. 2. AI Technologies Uses for Diagnostic Modalities in Drug Resistant Tuberculosis Diagnosis. 3. From Preprocessing to Prediction: An Analytical Study on Diabetes Data. 4. Integrating AI-Powered Multi-Modal Data for Early Cardiovascular Disease Detection and Personalized Predictive Healthcare. 5. Role of AI Technology in the Diagnosis of Urinary Tract Infection. 6. Evolving Role of Healthcare Professionals in Smart Healthcare. 7. Deep Learning-Based Stratification of Iron Overload in Thalassemia Patients. 8. Comparative Analysis of Automated Malaria Cell Classification: EfficientNet-B0 Transfer Learning vs. Traditional Machine Learning. 9. Deep CNN Optimization Method for MRI Image-Based Brain Tumor Identifications. 10. Tech-Enabled Transformations in Gender-Inclusive Healthcare: A Critical Interpretive Synthesis of Artificial Intelligence in India. 11. Healthcare AI Optimizing Diagnosis and Treatment Plans: AI Driven Precision Medicine and Personalized Healthcare. 12. A Comparative Analysis of Supervised and Semi-Supervised Deep Learning Models for Monkeypox Blisters Classification. 13. Hybrid Machine Learning Algorithms for Early Prediction of Diabetes. 14. Nature Inspired Algorithms of Machine Learning and Generative AI for Cancer Diagnosis and Treatment. 15. Future Trends in AI and Healthcare.
About the author
Dr. Syed Nisar Hussain Bukhari is an accomplished academician and researcher, currently serving as Scientist-D at the National Institute of Electronics and Information Technology (NIELIT), Srinagar-an institute under the Ministry of Electronics and Information Technology (MeitY), Government of India. He brings over twelve years of experience in teaching, research, and institutional leadership, with a specialized focus on Artificial Intelligence, Machine Learning, Deep Learning, and their interdisciplinary applications.
