Limited Offer
Save 50% on book bundles
Immediately download your ebook while waiting for your print delivery. No promo code needed.
Responsible and Explainable Artificial Intelligence in Healthcare
Ethics and Transparency at the Intersection
- 1st Edition - December 1, 2024
- Editors: Akansha Singh, Krishna Kant Singh, Ivan Izonin
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 4 7 8 8 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 4 7 8 9 - 7
Responsible and Explainable Artificial Intelligence in Healthcare: Ethics and Transparency at the Intersection provides clear guidance on building trustworthy Artificial Intell… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteResponsible 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.
- Gives insights into the responsible and explainable use of Artificial Intelligence in healthcare and explore the challenges and opportunities for promoting ethical and transparent practices in this field
- Offers the solution to strike a balance between patient privacy and data exchange
- Provides concrete advice on how to create trustworthy, accountable, and transparent Artificial Intelligence systems
- Explains the moral and social effects of Artificial intelligence in healthcare and suggests ways to encourage its ethical application
Medical clinicians, researchers, healthcare providers and AI technologists
1. Revolutionizing Healthcare: The Transformative Role of Artificial Intelligence
2. Ethical Considerations in AI Powered Diagnosis and Treatment
3. Explainable AI Methods to Increase Trustworthiness in Healthcare
4. Designing Transparent and Accountable AI Systems for Healthcare
5. Ensuring Fairness and Mitigating Bias in Healthcare AI Systems
6. AI Enhanced Healthcare: Opportunities, Challenges, Ethical Considerations, and Future Risk
7. Healthcare Revolution: Advances in AI-Driven Medical Imaging and Diagnosis
8. A Deep Learning Approach for Medical Image Classification Using Xai and Convolutional Neural Networks
9. Hybrid Ensemble Learning Model to Improve the Performance and Interpretability of Medical Diagnosis: Small Data Tasks
10. Legal and Regulatory Issues Related to AI in Healthcare
11. Responsible and Explainable Artificial Intelligence in Healthcare: Conclusion and Future Directions
2. Ethical Considerations in AI Powered Diagnosis and Treatment
3. Explainable AI Methods to Increase Trustworthiness in Healthcare
4. Designing Transparent and Accountable AI Systems for Healthcare
5. Ensuring Fairness and Mitigating Bias in Healthcare AI Systems
6. AI Enhanced Healthcare: Opportunities, Challenges, Ethical Considerations, and Future Risk
7. Healthcare Revolution: Advances in AI-Driven Medical Imaging and Diagnosis
8. A Deep Learning Approach for Medical Image Classification Using Xai and Convolutional Neural Networks
9. Hybrid Ensemble Learning Model to Improve the Performance and Interpretability of Medical Diagnosis: Small Data Tasks
10. Legal and Regulatory Issues Related to AI in Healthcare
11. Responsible and Explainable Artificial Intelligence in Healthcare: Conclusion and Future Directions
- No. of pages: 320
- Language: English
- Edition: 1
- Published: December 1, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780443247880
- eBook ISBN: 9780443247897
AS
Akansha Singh
Prof. Akansha Singh, Professor at the School of Computer Science and Engineering, Bennett University, Greater Noida, boasts a comprehensive academic background with a B.Tech, M.Tech, and Ph.D. in Computer Science. Her doctoral studies, conducted at the prestigious IIT Roorkee, were focused on the cutting-edge fields of image processing and machine learning. A prolific author and scholar, Dr. Singh has contributed over 100 research papers and penned more than 25 books. Her editorial expertise is recognized by leading publishers such as Elsevier, Taylor and Francis, and Wiley, where she has edited books on a variety of emerging topics.Dr. Singh serves as the Associate Editor in IEEE Access, Discover Applied Science, PLOS One and guest editor in several journals. Her research interests are diverse and influential, spanning image processing, remote sensing, the Internet of Things (IoT), Blockchain and machine learning. Prof. Singh’s work in these areas not only advances the field of computer science but also significantly contributes to the broader scientific and technological community.
Affiliations and expertise
School of CSET, Bennett University, Greater Noida, IndiaKS
Krishna Kant Singh
Dr. Krishna Kant Singh, currently the esteemed Director of Delhi Technical Campus in Greater Noida, India, is a highly experienced educator and researcher in the field of engineering and technology. He is a B.Tech and M.Tech degree, a Postgraduate Diploma in Machine Learning and Artificial Intelligence from IIIT Bangalore, a Master of Science in Machine Learning and Artificial Intelligence from Liverpool John Moores University, United Kingdom, and a Ph.D. from IIT Roorkee. Dr. Singh has made significant contributions to the academic and research community. With over 19 years of teaching experience, he has played a vital role in educating and mentoring future professionals. Dr. Singh also serves as an Associate Editor at IEEE Access, an Editorial Board Member at Applied Computing and Geosciences (Elsevier), and a Guest Editor for Complex and Intelligent Systems. His extensive publication record includes over 132 research papers. His areas of interest include Machine Learning, Deep Learning, computer vision and so on.
Affiliations and expertise
Delhi Technical Campus, Greater Noida, IndiaII
Ivan Izonin
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 authored several research papers, including chapters in books. His work on text mining and natural language processing has been widely cited in the academic community. Dr. Izonin is well-respected in his field and has served as an Editor, Guest Editor, reviewer for several international journals and conferences
Affiliations and expertise
Lviv Polytechnic National University, Lviv, Ukraine