About the Book
Explainable AI in Clinical Practice
Table of Contents:
1. Foundations of AI in Healthcare
2. Sustainable Health Record System Using Artificial Intelligence with Blockchain Technology: A Recent Trends and Future Research Perspective
3. Unleashing the Hidden Potential of Metaverse in Healthcare: A Bibliometric Analysis and Future Research Agenda
4. Interpretability in Clinical Sentiment Analysis: A Comparative Study of LIME, SHAP, and Grad-CAM in Large Language Models
5. Enhancing Clinical Documentation Through Explainable AI-Driven Natural Language Processing (NLP): Improving Transparency, Accuracy, and Compliance in Medical Record-Keeping
6. Smart AI-Driven Treatment Planning: Transparency and Discovering New Innovations in the Modern Medical Field
7. An Integrated Framework for Dengue Fever Prediction Using CNN with SHAP
8. Atrial Fibrillation Classification Using Rectangular Pulse and Cascade Hybrid Multilayer Perceptron (CHMLP) Neural Network
9. Cascade Hybrid Multilayer Perceptron Network for ECG Signal Pattern Recognition Applications
10. Interpretable Artificial Intelligence for Medical Imaging and Diagnostics
11. Leveraging Data Analytics for Better Patient Care and Operational Effectiveness in Hospitals
12. Smart Therapeutic Systems: The Role of Artificial Intelligence in Personalized Mental Health Care and Patient Supervision
13. Explainable AI for Malaria Classification: Enhancing Transparency and Trust in Clinical Diagnostics
14. Transparency in AI-Driven Healthcare: The Role of XAI in Enhancing Fairness and Mitigating Bias in Clinical Practice
15. The Transparent Heart: XAI in Cardiology
16. IoT-Enabled Smart Healthcare for Multiple Sclerosis: Trends, Challenges, and Future Directions
17. Self-guided Medication System using Hybrid Model of Graph Neural Networks with LIME
18. AI Bias & Fairness in Clinical Applications
19. Enhancing Trust in Deep Learning Diagnostics: The Role of Explainable AI in Medical Image Analysis
20. Emerging Trends in Artificial Intelligence in Drug Design and Development: Revolutionizing Clinical Practices
21. Emerging Trends and Technologies in Explainable AI (XAI) for Clinical Practice
22. Ethic of Transparant AI in Physiotherapy
23. Future Research Opportunities Towards Using XAI in Healthcare
About the Author :
Dr. Saurav Mallik is a Research Scientist in the Department of Pharmacology and Toxicology at The University of Arizona, USA. He previously served as a Postdoctoral Fellow at Harvard T.H. Chan School of Public Health (2019-2022) and held positions at the University of Texas Health Science Center at Houston (2018-2019) and the University of Miami Miller School of Medicine (2017-2018). Dr. Mallik earned his PhD in Computer Science and Engineering from Jadavpur University, India, in 2017, conducting research at the Indian Statistical Institute. He received a Research Associateship from CSIR, India, in 2017. With over 150 publications in high-impact journals, he has authored several books and patents. Dr. Mallik is an active member of IEEE, ACM, AACR, and Bioclues, and has collaborated with editors and reviewers for prestigious journals. His research focuses on Computational Biology, Bioinformatics, Bio-Statistics, and Machine Learning. Arvind Panwar is a researcher and academic in the field of Computer Science and Engineering whose interests include blockchain technology, information security, cybersecurity, data analytics, and emerging digital technologies. His research focuses on the development of secure and scalable computing frameworks, including applications of blockchain in healthcare and data management. Dr. Panwar has contributed to scholarly research through journal articles, conference papers, book chapters, patents, and edited volumes. He is actively engaged in research, innovation, and academic collaboration, with work spanning blockchain, artificial intelligence, the Internet of Things, and cybersecurity. His activities include mentoring students, supporting interdisciplinary research initiatives, and participating in international academic collaborations. Through his research and educational contributions, he promotes the translation of advanced computing technologies into practical solutions for industry and society.
Dr. Achin Jain is a researcher and academic specializing in the application of artificial intelligence to healthcare. His research focuses on machine learning, deep learning, computational intelligence, medical image analysis, and AI-enabled approaches to disease diagnosis. He has contributed extensively to the scholarly literature through journal articles, conference publications, and book chapters addressing the use of AI in medical and healthcare applications.
Dr. Jain is actively involved in mentoring graduate students and leading interdisciplinary research activities that bring together expertise from computing and healthcare domains. He also promotes national and international research collaborations aimed at advancing innovative AI solutions for medical challenges. His work centers on developing and evaluating intelligent methods that support clinical decision-making and enhance healthcare outcomes through the responsible application of artificial intelligence. Dr. Aimin Li is an associate professor of Xi'an University of Technology, China. He got his master degree from Xi'an University of Technology, and doctoral degree from Xidian University. He previously worked as a visiting scientist in University of Texas Health Science Center, Houston, Texas, USA. His current research applications are in the areas of machine learning, bioinformatics, and regulatory networks. He has published 80+ research papers. He is also an editor of International Journal of Computational Biology and Drug Design, PC member of ICIBM (International Conference on Intelligent Biology and Medicine), and co-chair of BIBM IWRI 2020 Assoc. Prof. Dr. Korhan Cengiz is a senior researcher at the University of Hradec Králové, Czech Republic, and Associate Professor at Istinye University, Turkey. He holds a PhD in Electronics Engineering from Kadir Has University and has held academic roles in Turkey, the UAE, and Jordan. Dr. Cengiz has authored over 40 SCI/SCI-E articles, 10+ book chapters, 5 international patents, and edited more than 20 books. His research focuses on wireless sensor networks, IoT, signal processing, and 5G. He serves as Associate Editor for IEEE Transactions on Intelligent Transportation Systems, IEEE Potentials, and IET journals, and is a frequent keynote speaker at IEEE and Springer conferences. A Senior Member of IEEE and ACM, he has received multiple awards, including best paper and presentation honors at ICAT conferences.
