The Evolution of Artificial Intelligence in Healthcare
From Basic Methods to Clinical Practice
Will be published approx. on 1. April 2027
Book
Paperback/Softback
330 pages
978-0-443-33066-7 (ISBN)
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.
More details
Language
English
Place of publication
Philadelphia
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 235 mm
Width: 191 mm
Weight
450 gr
ISBN-13
978-0-443-33066-7 (9780443330667)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Person
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).
Content
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