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Responsible and Explainable Artificial Intelligence in Healthcare
Ethics and Transparency at the Intersection
- 1st Edition - November 14, 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
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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
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter 1. Revolutionizing healthcare: The transformative role of artificial intelligence
- Intoduction
- Artificial Intelligence
- Expert systems
- Fuzzy logic
- Artificial neural networks
- Machine learning
- Natural language processing
- Computer vision
- Robotics
- Deep learning
- Healthcare
- Artificial intelligence today
- Medium-term AI
- Long-term
- Connected/augmented care
- Precision diagnostics
- Precision therapeutics
- Precision medicine
- International trends
- Stages of artificial intelligence in medical procedures
- Locating the problematic region that requires intervention
- Eliminate the following possibilities
- Clinical studies that are both more effective and faster
- Developing diagnostic biomarkers in order to identify diseases
- AI's potential and advantages in healthcare
- Requirement of experts
- Issues and challenges in the adoption of AI in healthcare
- Reasonableness and imbalance
- Conclusion
- Chapter 2. Ethical considerations in AI powered diagnosis and treatment
- Introduction
- Methodology
- Ethical issues
- Ethical issue one
- Ethical issue two
- Ethical issue three
- Ethical issue four
- Ethical issue five
- Ethical issue six
- Supplementary ethical issue
- Strategies for solving ethical issues
- Applications of AI in the health domain
- Conclusion
- Chapter 3. Explainable AI methods to increase trustworthiness in healthcare
- Introduction
- Methodology
- Data analysis and visualization
- Description of the classification task
- Age
- Sex
- Chest pain type
- Resting BP
- Cholesterol
- Fasting BS
- Resting ECG (electrocardiogram)
- Max HR
- Exercise angina
- Oldpeak
- ST slope
- Data sample
- Features of the data set
- Explaining with data visualization
- Target class
- Correlation between features
- Comparison of AI and XAI methods for classification task in healthcare
- Types of models
- Model creation
- Neural network
- Model comparison
- Explaining with permutation feature importance and SHAP
- Recursive feature selection
- Decision boundary
- Explaining with decision tree machine learning model
- Conclusions
- Chapter 4. Designing transparent and accountable AI systems for healthcare
- Introduction
- Exploring artificial intelligence
- AI and ML: Revolutionizing healthcare
- Understanding the importance of transparency in healthcare AI
- Ethical considerations for AI in medical settings
- Challenges of implementing AI and ML in the medical field
- Navigating the ethical landscape: A review of transparent and accountable AI systems in healthcare
- Applications of AI in healthcare: Enhancing transparency and accountability
- Accountability, transparency and explainability in AI for healthcare
- Understanding explainability in cardiac arrest prediction tools
- Unlocking the Black Box: exploring interpretability methods for deep neural networks in medical image analysis
- AI-aided drug discovery efforts
- Transforming healthcare: Leveraging artificial intelligence in clinical practice
- The use of AI in healthcare: Past, present, and future
- Frameworks for designing transparent AI systems
- Enhancing biomedical data sharing and privacy framework
- Condensing and rephrasing the ethical and responsible practices framework
- Accountability and transparency framework
- Conclusion
- Chapter 5. Ensuring fairness and mitigating bias in healthcare AI systems
- Introduction to fairness in healthcare AI
- The role of AI in healthcare
- The promise of AI: Enhancements in patient care
- The challenge of bias: Risks and consequences
- The importance of fair AI systems
- Understanding bias in AI
- Types of bias in AI systems
- Sources of bias in healthcare AI systems
- Identifying sources of bias
- Impact of bias
- Strategies for mitigating bias
- Fairness in AI
- Measuring fairness in AI systems
- Implementing fairness in AI systems
- Conclusion
- Chapter 6. AI enhanced healthcare: Opportunities, challenges, ethical considerations, and future risk
- Introduction
- Related work
- AI in healthcare
- Machine learning (ML)
- Natural language processing (NLP)
- Expert system
- Robots and robotic process
- Applying AI in healthcare
- Patient engagement and adherence applications
- Diagnosis and treatment applications
- Administrative applications
- Ethical principles in AI-powered diagnosis and treatment
- Autonomy
- Risk reduction and safety
- Privacy of data
- Accountability and liability
- Nondiscrimination and fairness principles
- Validity
- Trustworthiness
- Data quality enhancement
- Equity, inclusivity, and accessibility
- Collaboration
- Challenges of AI in healthcare
- Limited data
- Data bias
- Lack of transparency
- Lack of standardization
- Lack of understanding
- Security and data privacy
- Fear of change and lack of trust in AI
- High cost
- Future of AI in health care
- Conclusion
- Chapter 7. Healthcare revolution: Advances in AI-driven medical imaging and diagnosis
- Introduction
- Technological advancements
- AI in healthcare
- Artificial intelligence and medical visualization
- CV for surgery and diagnosis
- Healthcare using AR and VR
- Patient experience
- Intelligent personal health records
- Health monitoring and wearables
- Natural language processing
- Personal records integration
- Smart devices
- Minimally invasive surgery
- Neuroprosthetics
- Ambient assisted living
- Smart home
- Assistive robots
- Cognitive assistants
- Social and emotional stimulation
- Chapter 8. A deep learning approach for medical image classification using XAI and convolutional neural networks
- Introduction
- State-of-the-arts
- Materials and methods
- Analysis of selected datasets
- The proposed model architecture
- Training, validation and evaluation of models
- Modeling and results
- Model development and analysis tools
- Results of applying the proposed algorithm for Dataset 1
- Results of applying the proposed algorithm for Dataset 2
- Analyzing the behavior of classification models when applying XAI methods
- Discussion results for Dataset 1
- Discussion results for Dataset 2
- Conclusions
- Funding
- Chapter 9. Hybrid ensemble learning model to improve the performance and interpretability of medical diagnosis: Small data tasks
- Introduction
- Background
- Naïve Bayes
- SGTM neural-like structures
- Ito decomposition
- Procedure for the synthesis of linear polynomial
- Hybrid cascade-based prediction model and its algorithmic implementations
- Design of the hybrid cascade-based ensemble learning model with improved performance and interpretability
- First algorithmic implementation of the proposed method (Algorithm 1)
- Second algorithmic implementation of the proposed method (Algorithm 2)
- Third algorithmic implementation of the proposed method (Algorithm 3)
- Datasets description and their preprocessing
- Dataset 1
- Dataset 2
- Dataset 3
- Balancing techniques for small data analysis
- Modeling and results
- Data balancing results
- Implementation results of the Algorithm 1
- Implementation results of the Algorithm 2
- Implementation results of the Algorithm 3
- Comparison and discussion
- Comparison of the accuracy of all investigated methods
- Comparison of the training duration for all investigated methods
- Comparison of the interpretability of all investigated methods
- Conclusions
- Funding
- Chapter 10. Legal and regulatory issues related to AI in healthcare
- Introduction
- The rise of AI in healthcare
- AI in healthcare market trends
- Legal and regulatory landscape
- Legal and ethical problems raised
- Importance of resolving legal and regulatory matters
- Benefits of AI in healthcare
- Challenges faced with legal issues of AI in healthcare
- AI in healthcare: A gift or a threat?
- Mainstream use cases involving AI's legal and regulatory concerns in healthcare
- Legal and ethical concerns in the realm of reproductive health within the field of neurology
- HIV-related public health, legal, and moral concerns pertaining to expectant mothers and new-borns
- Juvenile justice: Legal concerns, rights, and ethics for mental health
- Concerns about law and ethics in college mental health
- Treating adolescent patients in AI in healthcare: Legal and ethical considerations
- The social, legal, and ethical implications of AI to the treatment of breast cancer
- Safeguarding patients' personal information
- “Doctor ChatGPT”: LLMs and a request for regulation
- Legal and regulatory issues with IOT technology and wearables
- AI legal: Concerns related to a scam, waste, and exploitation prevention
- Conclusion
- Chapter 11. Responsible and explainable artificial intelligence in healthcare: Conclusion and future directions
- Introduction
- Ethical foundations
- Case studies and applications
- Technological advancements
- Major challenges and resolutions
- Data privacy and security
- Bias and fairness
- Regulatory frameworks
- Future directions
- Technological advances in AI
- Interdisciplinary collaboration
- Patient-centered approaches
- Conclusion: Shaping the future of AI in healthcare
- Call to action
- Vision for the future
- Index
- No. of pages: 320
- Language: English
- Edition: 1
- Published: November 14, 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, UkraineRead Responsible and Explainable Artificial Intelligence in Healthcare on ScienceDirect