Responsible and Explainable Artificial Intelligence in Healthcare: Ethics and Transparency at the Intersection
Editat de Akansha Singh, Krishna Kant Singh, Ivan Izoninen Limba Engleză Paperback – 18 noi 2024
- 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
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Specificații
ISBN-13: 9780443247880
ISBN-10: 0443247889
Pagini: 314
Dimensiuni: 152 x 229 mm
Greutate: 0.45 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0443247889
Pagini: 314
Dimensiuni: 152 x 229 mm
Greutate: 0.45 kg
Editura: ELSEVIER SCIENCE
Cuprins
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