Author(s) Name:  Lei Xing, Maryellen L. Giger, James K. Min

About the Book:

   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.

Table of Contents

Introduction

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

Technical basis

  3. Deep learning for biomedical videos: perspective and recommendations
  4. Biomedical imaging and analysis through deep learning
  5. Expert systems in medicine

Clinical applications

  6. Electronic health record data mining for artificial intelligence healthcare
  7. Roles of artificial intelligence in wellness, healthy living, and healthy status sensing
  8. The growing significance of smartphone apps in data-driven clinical decision-making: Challenges and pitfalls
  9. Artifical intelligence for pathology
  10. Artificial intelligence—enabled public health surveillance—from local detection to global epidemic monitoring and control

Future outlook

  11. Regulatory, social, ethical, and legal issues of artificial intelligence in medicine
  12. Industry perspectives and commercial opportunities of artificial intelligence in medicine
  13. Outlook of the future landscape of artificial intelligence in medicine and new challenges

ISBN:  9780128212585

Publisher:  Elsevier

Year of Publication:  2020

Book Link:  Home Page Url