Revolutionizing healthcare: impact of artificial intelligence on diagnosis, treatment, and patient care

Table of contetns:
Front Matter
Pages i-ix
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A Systematic Review on Learning Based Models for Automatic Classification of Medical Documents
Suwarna Gothane, B. Sivaneasan, Amjan Shaik, Prasun Chakrabarti
Pages 1-11
A Novel Method for Automatic Detection of Ulcer in Wireless Capsule Endoscopy Images
C. P. Sindhu, Vysak Valsan
Pages 13-26
GAN-Enhanced Deep Learning Framework for High-Precision Lung Nodule Detection and Severity Assessment in CT Images
A. Asuntha, P. K. Dutta, K. Vino Aishwarya
Pages 27-56
Machine Learning Approach to Recognition Human Mental State from EEG Signal
Swati Chowdhuri, Ayushi Bhargav, Ayan Kumar Panja, Amartya Mukherjee
Pages 57-75
Maximizing Accuracy in Alzheimer’s Disease Prediction: A Optuna Hyper Parameter Optimization Strategy Using MRI Images
P. Deepan, G. Prabhakar Reddy, M. Arsha Reddy, R. Vidya, S. Dhiravidaselvi
Pages 77-91
Advanced Deep Learning Models for COVID-19 Prediction: A Multi-convolutional Neural Network Perspective
P. Deepan, Amjan Shaik, R. Santhoshkumar, B. Rajalingam, S. Dhiravidaselvi, N. Arul
Pages 93-109
A Parametric Analysis of Cardiovascular Diseases Detection Methods Using ML Techniques
Marri Sireesha, Anjaiah Adepu, Amjan Shaik, Naga Jyothi
Pages 111-123
Machine Learning Based Disease Classification and Prediction
Sandhya Nadella, Pushkara Annaldas, Sai Aashritha Kalakancheri, Charan Sai Kamsani, Jagruthi Mekala
Pages 125-137
Segmentation of Retinal Blood Vessels and Optic Disc Using Deep Neural Networks: State-Of-The-Art Review
Saba Sheiba, M. Neelakantappa, Amjan Shaik
Pages 139-152
Enhancing Brain Tumor Detection in MR Images Using Modified Few-Shot Learning
Akkipalli Sowjanya, Amjan Shaik
Pages 153-163
An Empirical Study on Lung Cancer Detection and Classification Using Machine Learning and Image Processing Techniques
Kabir Ahmed, Syed Sazzad Ahmed, Abhishek Talukdar, Dipraj Chakrabarty
Pages 165-176
Survey on Depression Detection Using Machine Learning Techniques⋆
Kritika Shrivastava, Arunima Jaiswal, Nitin Sachdeva
Pages 177-187
Generative AI for Enhanced Decision Making in Healthcare 5.0
C. Rajan, H. Blankson
Pages 189-197
Integration of Generative AI System to IoT Based Healthcare Systems 5.0
Janjhyam Venkata Naga Ramesh, Veera Talukdar, Ardhariksa Zukhruf Kurniullah, Sanjiv Kumar Jain, Rohit Anand, Ankur Gupta
Pages 199-217
Beyond Prediction: How Generative AI is Creating New Healthcare Realities
Bablu Pramanik, Debosree Ghosh, Debasree Ghosh, Kaushik Paul
Pages 219-233
Vertical Integration of Data Augmentation Using Generative AI in Medical Imagining for Population Health: Deep Learning Approaches for Transforming Health 5.0
Bhupinder Singh, Pushan Kumar Dutta, Christian Kaunert
Pages 235-253
Securing Healthcare AI: Applied Federal Learning
Md. Nurul Huda, Mohammad Badruddoza Talukder, Sanjeev Kumar
Pages 255-272
Enhancing Healthcare Operations: Harnessing Generative AI for Efficiency and Innovation
Pawan Whig, Pushan Kumar Dutta, Nikhitha Yathiraju, Pronaya Bhattacharya, Iti Batra
Pages 273-287
Transforming Healthcare Operations with Generative AI: Optimizing Resource Allocation, Workflow Management, and Predictive Analytics
Pawan Whig, Pushan Kumar Dutta, Nikhitha Yathiraju, Pronaya Bhattacharya, Seema Sharma
Pages 289-308

[https://link.springer.com/book/10.1007/978-3-031-75771-6]

This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpoint—coupled with case studies, statistical analyses, and expert insights—the book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI.

(https://link.springer.com/book/10.1007/978-3-031-75771-6)