Explainable AI in Clinical Practice
Advanced Applications and Future Directions
- 1st Edition - July 1, 2026
- Latest edition
- Editors: Saurav Mallik, Arvind Panwar, Achin Jain, Aimin Li, Korhan Cengiz
- Language: English
Explainable AI in Clinical Practice: Advanced Applications and Future Directions builds on foundational concepts to explore the practical implementation and emerging trends of tra… Read more
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.
- Provides comprehensive implementation frameworks that guide the deployment of explainable AI in healthcare, addressing technical, organizational, ethical, and regulatory challenges
- Presents detailed, specialty-specific case studies that demonstrate successful real-world applications of explainable AI across various clinical disciplines
- Explores future directions and emerging technologies, offering insights into how explainable AI will integrate with innovations like federated learning and multimodal systems to shape healthcare’s evolution
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
- Edition: 1
- Latest edition
- Published: July 1, 2026
- Language: English
SM
Saurav Mallik
AP
Arvind Panwar
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.
AJ
Achin Jain
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.
AL
Aimin Li
KC