Responsible and Explainable Artificial Intelligence in Healthcare by Singh, Akansha; Singh, Krishna Kant; Izonin, Ivan;

Ethics and Transparency at the Intersection

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A termék adatai:

Kiadó Elsevier Science
Megjelenés dátuma 2024. november 18.

ISBN 9780443247880
Kötéstípus Puhakötés
Terjedelem314 oldal
Méret 228x152 mm
Súly 510 g
Nyelv angol

Kategóriák

A biológia általános kérdései A biológia általános kérdései (karitatív célú kampány)

Hosszú leírás:

"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."

Több

Tartalomjegyzék:

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

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