Artificial Intelligence in Medicine: Technical Basis and Clinical Applications

Artificial Intelligence in Medicine: Technical Basis and Clinical Applications
  • eBook:
    Artificial Intelligence in Medicine: Technical Basis and Clinical Applications
  • Author:
    Lei Xing, Maryellen L. Giger, James K. Min
  • Edition:
    1 edition
  • Categories:
  • Data:
    September 17, 2020
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    568 pages
  • Format:

Book Description
Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI.
The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticism. By understanding how AI works, and developing an appreciation for both limitations and strengths, clinicians can harness its computational power to streamline workflow and improve patient care. It also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches. On the other hand, computers scientists and data analysts can provide solutions, but often lack easy access to clinical insight that may help focus their efforts. This book provides vital background knowledge to help bring these two groups together, and to engage in more streamlined dialogue to yield productive collaborative solutions in the field of medicine.


I - Introduction
1. Artificial intelligence in medicine: past, present, and future
2. Artificial intelligence in medicine: Technical basis and clinical applications

II - Technical basis
3. Deep learning for biomedical videos: perspective and recommendations
4. Biomedical imaging and analysis through deep learning
5. Expert systems in medicine
6. Privacy-preserving collaborative deep
learning methods for multiinstitutional
training without sharing patient data
7. Analytics methods and tools for
integration of biomedical data in

III - Clinical applications
8. Electronic health record data mining for artificial intelligence healthcare
9. Roles of artificial intelligence in wellness, healthy living, and healthy status sensing
10. The growing significance of smartphone apps in data-driven clinical decision-making: Challenges and pitfalls
11. Artificial intelligence for pathology
12. The potential of deep learning for gastrointestinal endoscopy—a disruptive new technology
13. Lessons learnt from harnessing deep learning for real-world clinical applications in ophthalmology: detecting diabetic retinopathy from retinal fundus photographs
14. Artificial intelligence in radiology
15. Artificial intelligence and interpretations in breast cancer imaging
16. Prospect and adversity of artificial intelligence in urology
17. Meaningful incorporation of artificial intelligence for personalized patient management during cancer: Quantitative imaging, risk assessment, and therapeutic outcomes
18. Artificial intelligence in oncology
19. Artificial intelligence in cardiovascular imaging
20. Artificial intelligence as applied to clinical neurological conditions
21. Harnessing the potential of artificial neural networks for pediatric patient management
22. Artificial intelligenceenabled public health surveillance—from local detection to global epidemic monitoring and control

IV - Future outlook
23. Regulatory, social, ethical, and legal issues of artificial intelligence in medicine
24. Industry perspectives and commercial opportunities of artificial intelligence in medicine
25. Outlook of the future landscape of artificial intelligence in medicine and new challenges

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