Artificial Intelligence in Medicine

Technical Basis and Clinical Applications
Erscheinungsjahr 2020
ISBN: 978-0-12-821259-2
Verlag: Elsevier Science

Buch, Englisch, 568 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 1130 g

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.

Zielgruppe

Clinicians, medical doctors, bioinformaticians, bioengineers, data scientists

Autoren/Hrsg.

Xing, Lei

Giger, Maryellen L

Min, James K.

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Telemedizin, e-Health
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Fertigungsindustrie Spitzentechnologiesektor
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Public Health, Gesundheitsmanagement, Gesundheitsökonomie, Gesundheitspolitik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik

Inhaltsverzeichnis

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

Über Autor(innen)

Xing, Lei
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.

Min, James K.
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.