Artificial Intelligence in Medicine

Technical Basis and Clinical Applications
Herausgeber: Xing, Lei; Min, James K; Giger, Maryellen L

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 568
• Erscheinungstermin: 3. September 2020
• Englisch
• Abmessung: 232mm x 191mm x 30mm
• Gewicht: 1172g
• ISBN-13: 9780128212592
• ISBN-10: 0128212594
• Artikelnr.: 59989825

Inhaltsangabe

I Introduction
1. Artificial intelligence in medicine: past, present, and future2.
Artificial intelligence in medicine: Technical basis and clinical
applications
II Technical basis
3. Deep learning for biomedical videos: perspective and recommendations4.
Biomedical imaging and analysis through deep learning5. Expert systems in
medicine6. Privacy-preserving collaborative deep learning methods for
multiinstitutional training without sharing patient data7. Analytics
methods and tools for integration of biomedical data in medicine
III Clinical applications
8. Electronic health record data mining for artificial intelligence
healthcare9. Roles of artificial intelligence in wellness, healthy living,
and healthy status sensing10. The growing significance of smartphone apps
in data-driven clinical decision-making: Challenges and pitfalls11.
Artifical intelligence for pathology12. The potential of deep learning for
gastrointestinal endoscopy-a disruptive new technology13. Lessons learnt
from harnessing deep learning for real-world clinical applications in
ophthalmology: detecting diabetic retinopathy from retinal fundus
photographs14. Artificial intelligence in radiology15. Artificial
intelligence and interpretations in breast cancer imaging 16. Prospect and
adversity of artificial intelligence in urology17. Meaningful incorporation
of artificial intelligence for personalized patient management during
cancer: Quantitative imaging, risk assessment, and therapeutic outcomes18.
Artificial intelligence in oncology19. Artificial intelligence in
cardiovascular imaging20. Artificial intelligence as applied to clinical
neurological conditions21. Harnessing the potential of artificial neural
networks for pediatric patient management22. Artificial
intelligence-enabled 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 medicine24. Industry perspectives and commercial
opportunities of artificial intelligence in medicine25. Outlook of the
future landscape of artificial intelligence in medicine and new challenges