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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
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 medicine
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. Artifical 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 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 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
- No. of pages:
- © Academic Press 2021
- 3rd September 2020
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Dr. Lei 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 artificial intelligence in medicine, medical imaging, treatment planning, molecular imaging instrumentations, image guided interventions, and nanomedicine. He has made unique and significant contributions to each of the above areas. Dr. Xing is an author on more than 400 peer reviewed publications, a co-inventor on many issued and pending patents, and a principal investigator on numerous NIH, ACS, DOD, AAPM, RSNA and corporate grants. He is a fellow of AAPM (American Association of Physicists in Medicine) and AIMBE (American Institute for Medical and Biological Engineering). He has received numerous awards from various societies and organizations for his work in artificial intelligence, medical physics and medical imaging.
Radiation Oncology Department, Stanford University, Stanford, CA, United States
Dr. 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 in the Department of Radiology for Basic Science Research. Dr. Giger is one of the pioneers in the field of CAD (computer-aided diagnosis) and her artificial intelligence research in cancer imaging for risk assessment, diagnosis, prognosis, and therapeutic response has yielded various translated components, including the use of these “virtual biopsies” in imaging-genomics association studies. She is a recipient of multiple NIH, DOD, and other grants, has authored more than 240 peer-reviewed journal papers, and is inventor on 30 patents. Dr. Giger is a member of the National Academy of Engineering (NAE) of the National Academies; Fellow of AAPM, AIMBE, SPIE, SBMR, IEEE, and IAMBE; recipient of the William D. Coolidge Gold Medal from the AAPM; a former president of AAPM and of SPIE; and is the current Editor-in-Chief of the Journal of Medical Imaging. She was cofounder of Quantitative Insights [now Qlarity Imaging], which produced QuantX, the first FDA-cleared, machine-learning driven CADx system to aid in cancer diagnosis. Her lab focuses on the development of multimodality CAD, quantitative image analysis/machine learning methods, and radiomics for AI in medical imaging.
Committee on Medical Physics, The University of Chicago, Chicago, IL, United States
Dr. James K. Min is the founder and CEO of Cleerly, Inc. Prior to this, Dr. Min was a Professor of Radiology and Medicine at the Weill Cornell Medical College. He also served as the Director of the Dalio Institute of Cardiovascular Imaging at New York-Presbyterian Hospital. He is an expert in cardiovascular imaging, having led numerous multicenter clinical trials and applying artificial intelligence methods to improve diagnosis and prognostication of coronary heart disease. Dr. Min has published over 450 peer-reviewed journal papers and has been the recipient of continual NIH grants for nearly a decade. Dr. Min is a Fellow of the American College of Cardiology and the European Society of Cardiology, and a Master of the Society of Cardiovascular Computed Tomography. He has received numerous awards from professional societies for his work in cardiovascular imaging and coronary heart disease. In his current role at Cleerly, Dr. Min is dedicating his efforts to developing end-to-end AI-based care pathways to prevent heart attacks.
Weill Cornell Medical College, New York, NY, United States Dalio Institute of Cardiovascular Imaging, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, NY, United States
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