Responsible and Explainable Artificial Intelligence in Healthcare Ethics and Transparency at the Intersection

Responsible and Explainable Artificial Intelligence in Healthcare: Ethics and Transparency at the Intersection provides clear guidance on building trustworthy Artificial Intelligence systems for healthcare. The book focuses on using Artificial Intelligence to improve diagnosis, prevent diseases, and personalize patient care. It addresses potential drawbacks, like reduced human interaction and ethical concerns, offering solutions for ethical and transparent Artificial Intelligence use in medicine. Across eight chapters, the book explores Artificial Intelligence's current status, its importance, and associated risks in healthcare. It explains designing reliable Artificial Intelligence for healthcare, tackling biases, and safeguarding patient privacy in the age of big data. The legal and regulatory landscape is also covered. One chapter is dedicated to showcasing real-world examples of responsible Artificial Intelligence in healthcare, highlighting best practices. The book concludes by summarizing key takeaways and discussing future challenges. "Responsible and Explainable Artificial Intelligence in Healthcare: Ethics and Transparency at the Intersection" is a valuable resource for healthcare professionals, policymakers, computer scientists, and ethicists concerned about Artificial Intelligence's ethical and societal impact on medicine.

1. Introduction to AI in Healthcare and Its Ethical Implications
2. The Importance of Explainability and Transparency in AI
3. Designing Transparent and Accountable AI Systems for Healthcare
4. Ensuring Fairness and Mitigating Bias in Healthcare AI Systems
5. Privacy and Security Considerations for Healthcare AI Systems
6. Legal and Regulatory Issues Related to AI in Healthcare
7. Case Studies in Responsible and Explainable AI in Healthcare
8. Conclusion and Future Directions

Dr. Krishna Kant Singh is working as Associate Professor in Electronics & Communication Engineering in KIET Group of Institutions, Delhi-NCR, India. He has wide teaching and research experience. Dr. Singh has acquired B.Tech, M.Tech, and Ph.D (IIT Roorkee) in the area of image processing and remote sensing. He has authored more than 70 research papers in Scopus and SCIE indexed journals of repute. He has also authored 25 technical books. He is also an associate editor of Journal of Intelligent & Fuzzy Systems (SCIE Indexed), IEEE ACCESS (SCIE Indexed) and Guest Editor of Open Computer Science. He is also member of Editorial board of Applied Computing & Geoscience (Elsevier).
Dr. Akansha Singh is B.Tech, M.Tech and PhD in Computer Science. She received her PhD from IIT Roorkee in the area of image processing and machine learning. Currently, she is working as Associate Professor in Department of Computer Science and Engineering, ASET, Amity University, Noida. She has to her credit more than 70 research papers, 20 books and numerous conference papers. She has been the editor for books on emerging topics with publishers like Elsevier, Taylor and Francis, Wiley etc. Dr. Singh has served as reviewer and technical committee member for multiple conferences and journals of High Repute. She is also the Associate Editor for IEEE Access journal which is an SCI journal with impact factor of 4.018. Dr. Singh has also undertaken government funded project as Principal Investigator. Her research areas include image processing, remote sensing, IoT and machine learning.
Dr. Ivan Izonin is an Associate Professor at the Department of Artificial Intelligence, Lviv Polytechnic National University, Ukraine. He holds a Ph.D. in computer science and has several years of experience in teaching, research, and development. Dr. Izonin's research interests include AI, healthcare, machine learning, and data mining. He has contributed to various international journals and conferences and has