The Evolution of Artificial Intelligence in Healthcare
From Basic Methods to Clinical Practice
- 1st Edition - April 1, 2027
- Latest edition
- Editor: Mario Cannataro
- Language: English
The Evolution of Artificial Intelligence in Healthcare: From Basic Methods to Clinical Practice explores revolutionary technological advancements in the medical and health… Read more
End of Summer Sale
Save up to 30% off
Bright savings for research, study, and discovery
Description
Description
The Evolution of Artificial Intelligence in Healthcare: From Basic Methods to Clinical Practice explores revolutionary technological advancements in the medical and healthcare realms due to generative AI and Deep Learning. This comprehensive guide not only explores cutting-edge technologies such as transformers and large language models for newcomers but also demystifies advanced applications like sequential decoding techniques and segmentation algorithms rarely explored in other literature. Sections cover foundational concepts and terminologies, explore deep learning and generative AI, provide AI's role in biomedical research, examine its integration into clinical practice, scrutinize its applications in public health, and discuss challenges and future prospects.
This is an indispensable resource for healthcare professionals, scientists, researchers, students, and enthusiasts seeking to deepen their understanding of this rapidly evolving field.
This is an indispensable resource for healthcare professionals, scientists, researchers, students, and enthusiasts seeking to deepen their understanding of this rapidly evolving field.
Key features
Key features
- Demonstrates the main opportunities of using AI in clinical practice and biomedical research
- Gives insights into the main challenges and risks of using AI in clinical practice and biomedical research
- Provides specific requirements for AI systems to be used in biomedical research and clinical practices
- Demonstrates legal and ethical aspects of AI systems
Readership
Readership
Medical Clinicians, Researchers, Healthcare Providers and AI Technologists
Table of contents
Table of contents
Part I. Artificial Intelligence. basic concepts and definitions
1. Machine Learning and Deep Learning
2. Artificial Neural Networks
3. Data Mining and Data Science
Part II. Artificial Intelligence. deep learning and generative AI
4. Transformers
5. Bidirectional Encoder Representations from Transformers (BERT)
6. Generative AI, Large Language Models
7. GPT. Generative Pre-trained Transformers
8. BARD
Part III. Artificial Intelligence in Biomedical Research
9. Bioinformatics methods and AI.
10. Network Science methods and AI
11. AI for investigating the molecular basis of diseases.
12. AI and Drug Repurposing
Part IV. Artificial Intelligence in Clinical Practice
13. AI based analysis of biosignals
14. AI-based analysis of bioimages
15. AI-based analysis of Medical Reports and Electronic Health Records
16. AI in surgery
17. AI in oncology
Part V. Artificial Intelligence in Public Health
18. One-Health AI
19. Virus diffusion prevention and management
Part VI.
20. Opportunities and Risks of Generative AI (GPT) in Medicine
21. Bias
22. Clinician and Dataset Shift
23. Explainability and Black Box models
24. Privacy and Security
25. Legal and ethical aspects
26. Integrating human and AI knowledge
1. Machine Learning and Deep Learning
2. Artificial Neural Networks
3. Data Mining and Data Science
Part II. Artificial Intelligence. deep learning and generative AI
4. Transformers
5. Bidirectional Encoder Representations from Transformers (BERT)
6. Generative AI, Large Language Models
7. GPT. Generative Pre-trained Transformers
8. BARD
Part III. Artificial Intelligence in Biomedical Research
9. Bioinformatics methods and AI.
10. Network Science methods and AI
11. AI for investigating the molecular basis of diseases.
12. AI and Drug Repurposing
Part IV. Artificial Intelligence in Clinical Practice
13. AI based analysis of biosignals
14. AI-based analysis of bioimages
15. AI-based analysis of Medical Reports and Electronic Health Records
16. AI in surgery
17. AI in oncology
Part V. Artificial Intelligence in Public Health
18. One-Health AI
19. Virus diffusion prevention and management
Part VI.
20. Opportunities and Risks of Generative AI (GPT) in Medicine
21. Bias
22. Clinician and Dataset Shift
23. Explainability and Black Box models
24. Privacy and Security
25. Legal and ethical aspects
26. Integrating human and AI knowledge
Product details
Product details
- Edition: 1
- Latest edition
- Published: April 1, 2027
- Language: English
About the editor
About the editor
MC
Mario Cannataro
Mario Cannataro is Full Professor of Computer Engineering and Bioinformatics at the University “Magna Graecia” of Catanzaro, Italy. He directs the Data Analytics Research Center and chairs the Bioinformatics Laboratory, leading interdisciplinary research at the interface of computing and life sciences. His research interests include bioinformatics, medical informatics, artificial intelligence, data analytics, sentiment analysis, and parallel and distributed computing. Professor Cannataro is actively involved in the international bioinformatics community through editorial, conference, and professional service activities, including participation in scientific committees, workshop organization, and journal editorial boards. He has contributed extensively to research and innovation in computational biology and health informatics and has authored numerous scholarly publications and books. He also serves on regional and national bodies related to bioinformatics, telemedicine, medical informatics, and research ethics, supporting collaboration and advancement across these fields.
Affiliations and expertise
University "Magna Græcia" of Catanzaro, Italy