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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.
- Provides history and overview of artificial intelligence, as narrated by pioneers in the field
- Discusses broad and deep background and updates on recent advances in both medicine and artificial intelligence that enabled the application of artificial intelligence
- Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach
Clinicians, medical doctors, bioinformaticians, bioengineers, data scientists
Part I: Introduction
1. Past and present of artificial intelligence medicine: From digital medicine to AI healthcare
2. A bird’s view of learning and clinical decision-making from medical data
Part II: Technical Basis
3. A primer of neural networks and deep learning for artificial intelligence medicine
4. Biomedical imaging and image analysis in the era of deep learning
5. Expert systems and artificial intelligence medicine
6. Machine learning platform and high performance computing for AIM (including distributed learning)
Part III: Clinical Applications
7. Electronic health record (EHR) and data mining for AI healthcare
8. Roles of artificial intelligence in wellness, healthy living, and healthy status sensing
9. Data science for deep genomics and biomedical data analysis (cell, genomics, protein-omics)
10. Digital and artificial intelligence pathology
11. Deep learning for endoscopy image analysis and disease detection and classification
12. Lessons learnt from the deep learning analysis of retinal fundus images and detection of diabetic retinopathy
13. Chest X-ray and CT image analysis and lung diseases classification
14. AI-assisted breast cancer detection and classification
15. Beyond natural image processing: computer vision for healthcare applications
16. Incorporating artificial intelligence in quantitative imaging and therapeutic outcome prediction
17. Interpretable machine learning for drug delivery and precision medicine
18. Artificial intelligence for radiation oncology applications
19. Applications of AI in the management of cardiovascular diseases
20. Artificial intelligence as applied to clinical neurological conditions
21. Harnessing the potential of artificial neural networks for pediatric patient management
22. AI-aided public-health surveillance: from local detection to global epidemic monitoring and control
Part IV: Challenges and Future directions
23. Regulatory, social, ethical, organisational and legal issues of AI medicine
24. Business perspectives and commercial opportunities of artificial intelligence medicine
25. Outlook of the future landscape of artificial intelligence and new challenges
- No. of pages:
- © Academic Press 2021
- 1st September 2020
- Academic Press
- Paperback ISBN:
Dr. Xing is currently the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. He also holds affiliate faculty positions in Department of Electrical engineering, Bio-X and Molecular Imaging Program at Stanford. Dr. Xing’s research has been focused on medical imaging, artificial intelligence in medicine, treatment planning, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. He has made unique and significant contributions to each of the above areas.
Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division, Radiation Oncology Department, Stanford University
Maryellen L. Giger is the A.N. Pritzker Professor of Radiology, the Committee on Medical Physics, and the College at the University of Chicago. She also serves as Vice-Chair for Basic Science Research in the Department of Radiology, University of Chicago. Dr. Giger is considered one of the pioneers in the development of CAD (computer-aided diagnosis). For 30 years, she has conducted federally-funded research on computer-aided diagnosis, including computer vision and machine learning, in the areas of breast cancer, lung cancer, prostate cancer, lupus, and bone diseases.
A.N. Pritzker Professor of Radiology, Committee on Medical Physics, University of Chicago, USA
James K. Min is Professor of Radiology at Weill Cornell Medical College, and Director of the Dalio Institute of Cardiovascular Imaging at NewYork-Presbyterian Hospital/Weill-Cornell Medical Center. Prior to this, he held the title of Professor of Medicine at both Cedars Sinai Medical Center in Los Angeles, CA and David Geffen UCLA School of Medicine, Los Angeles, CA. He is an expert in Cardiovascular Imaging and has led numerous multi-center international clinical trials. He has been studying clinical utility and coronary artery diseases for over ten years.
Professor of Radiology, Weill Cornell Medical College and Director, Dalio Institute of Cardiovascular Imaging, NewYork-Presbyterian Hospital/Weill-Cornell Medical Center
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