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...
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| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
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London, United Kingdom :
Academic Press,
[2025]
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Front Cover
- RESPONSIBLE AND EXPLAINABLE ARTIFICIAL INTELLIGENCE IN HEALTHCARE
- RESPONSIBLE AND EXPLAINABLE ARTIFICIAL INTELLIGENCE IN HEALTHCARE
- Copyright
- Contents
- Contributors
- 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
- AI-powered chatbots and digital personal assistants
- Ambient and intelligent care
- Precision diagnostics
- Diagnostic imaging
- Diabetic retinopathy screening
- Improving the precision and reducing waiting timings for radiotherapy planning
- Precision therapeutics
- Immunomics and synthetic biology
- AI-driven drug discovery
- Precision medicine
- New curative therapies
- AI-empowered healthcare professionals
- 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
- References
- Further reading
- 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
- References
- 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
- Acknowledgments
- References
- 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
- References
- 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
- References
- Further reading
- 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
- References.
- 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
- References
- Further reading
- 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
- Acknowledgments
- References
- 9
- Hybrid ensemble learning model to improve the performance and interpretability of medical diagnosis: Small data ...
- 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
- References
- 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.