The Evolution of Artificial Intelligence in Healthcare
From Basic Methods to Clinical Practice
- 1st Edition - August 1, 2026
- 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
Description
Description
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
Table of contents
Table of contents
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: August 1, 2026
- Language: English
About the editor
About the editor
MC
Mario Cannataro
Mario Cannataro is a Full Professor of Computer Engineering and Bioinformatics at University “Magna Graecia” of Catanzaro, Italy. He is the director of the Data Analytics research center and the chair of the Bioinformatics Laboratory. His current research interests include bioinformatics, medical informatics, artificial intelligence, sentiment analysis, data analytics, parallel and distributed computing. He is a member of the editorial boards of Briefings in Bioinformatics and IEEE/ACM Transactions on Computational Biology and Bioinformatics. He was guest editor of several special issues on bioinformatics and health informatics and organized several bioinformatics workshops in conjunction with ACM-BCB and IEEE-BIBM conferences. He has published three books and more than 300 papers in international journals and conference proceedings. Prof. Cannataro is a member of the Ethical Committee of the Calabria Region and a senior member of ACM, IEEE and SIBIM, He is currently a member of the steering committee of the Italian Bioinformatics Society (BITS) and of the Italian Association for Telemedicine and Medical Informatics (AITIM).