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Elsevier

Explainable AI in Clinical Practice

Advanced Applications and Future Directions

  • 1st Edition - July 13, 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

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Description

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.

Key features

  • 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

Readership

Healthcare professionals, AI researchers, biomedical engineers, and policymakers

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

Product details

  • Edition: 1
  • Latest edition
  • Published: July 23, 2026
  • Language: English

About the editors

SM

Saurav Mallik

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.
Affiliations and expertise
Research Scientist, Department of Pharmacology and Toxicology at The University of Arizona, USA

AP

Arvind Panwar

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.

Affiliations and expertise
University of Golgatta, India

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.

Affiliations and expertise
Bharati Vidyapeeth’s College of Engineering, India

AL

Aimin Li

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
Affiliations and expertise
Xi’an University of Technology, China

KC

Korhan Cengiz

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.
Affiliations and expertise
University Research Committee Chair, University of Fujairah, United Arab Emirates

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