Explainable AI in Clinical Practice

Advanced Applications and Future Directions
Erscheinungsjahr 2026
ISBN: 978-0-443-45374-8
Verlag: Elsevier Science

Buch, Englisch, 330 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

Explainable AI in Clinical Practice: Advanced Applications and Future Directions builds on foundational concepts to explore the practical implementation and emerging trends of transparent AI in healthcare. Featuring contributions from leading experts, this volume presents advanced methodologies, real-world case studies across various medical specialties, and strategies for overcoming ethical, regulatory, and operational challenges. The book offers comprehensive frameworks for integrating explainable AI into clinical workflows, emphasizing trust, patient understanding, and regulatory compliance. In addition, it examines future technologies such as federated learning, multimodal systems, and human-AI collaboration, providing insights into the evolving landscape of AI in medicine.

Essential for healthcare professionals, researchers, and policymakers, this volume aims to accelerate the responsible adoption of explainable AI, ultimately enhancing patient care, clinical decision-making, and healthcare system efficiency.

Autoren/Hrsg.

Mallik, Saurav

Panwar, Arvind

Jain, Achin

Li, Aimin

Cengiz, Korhan

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften

Inhaltsverzeichnis

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

Über Autor(innen)

Li, Aimin
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

Cengiz, Korhan
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.

Mallik, Saurav
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.

Panwar, Arvind
Dr. Arvind Panwar is a distinguished researcher and academician with over 15 years of experience in Computer Science and Engineering. He holds a Ph.D. from Guru Gobind Singh Indraprastha University, focusing on a secure cloud-based blockchain framework for health record management. His expertise includes blockchain technology, information security, cybersecurity, and data analytics.

Dr. Panwar has authored 9 SCI/SCOPUS-indexed journal articles, 15 conference papers, and 18 book chapters. He is currently editing three significant books: Data Analytics and Artificial Intelligence for Predictive Maintenance in Industry 4.0, Qubits Unveiled: Quantum Computing Solutions for Efficient Supply Logistics, and Energy Efficient Internet of Things-Based Wireless Sensor Networks. A prolific innovator, he holds 8 granted patents and 11 published patents related to blockchain, AI, and IoT applications. His contributions to mentoring graduate students and engaging in global collaborations, including a visiting professorship in Kazakhstan, further establish him as a leading figure in bridging research and industry.

Jain, Achin
Dr. Achin Jain is a distinguished researcher and academician with over 13 years of experience, specializing in Artificial Intelligence applications in healthcare. He holds a Ph.D. from Guru Gobind Singh Indraprastha University, where his research focused on designing feature selection methods for sentiment classification using Computational Intelligence Techniques. Dr. Jain’s expertise encompasses Machine Learning, Deep Learning, and advanced methodologies for Medical Image Analysis and AI-driven Disease Diagnosis. A prolific scholar, Dr. Jain has published 23 SCI/SCOPUS/ESCI- indexed journal articles, 10 conference papers, and 2 book chapters, with a strong emphasis on AI’s transformative role in medical diagnostics. He actively mentors graduate students, leads interdisciplinary research initiatives, and fosters international collaborations to advance AI innovations in healthcare. Dr. Jain’s contributions in merging technological advancements with medical applications highlight his dedication to leveraging AI for improving patient care, making him a leading voice in the field of AI-driven medical research.