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Artificial intelligence in medicine : technical basis and clinical applications /
Artificial Intelligence in 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...
| Clasificación: | Libro Electrónico |
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| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom :
Academic Press,
[2021]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Artificial intelligence in medicine : past, present and future / Efstathios D. Gennatas and Jonathan H. Chen
- Artificial intelligence in medicine: technical basis and clinical applications / Bradley J. Erickson
- Deep learning for biomedical videos : perspective and recommendations / David Ouyang, Zhenqin Wu, Bryan He and James Zou
- Biomedical imaging and analysis through deep learning / Karen Drukker, Pingkun Yan, Adam Sibley and Ge Wang
- Expert systems in medicine / Li Zhou and Margarita Sordo
- Privacy-preserving collaborative deep learning methods for multiinstitutional training without sharing patient data / Ken Chang, Praveer Singh, Praneeth Vepakomma, Maarten G. Poirot, Ramesh Raskar, Daniel L. Rubin and Jayashree Kalpathy-Cramer
- Analytics methods and tools for integration of biomedical data in medicine / Lin Zhang, Mehran Karimzadeh, Mattea Welch, Chris McIntosh and Bo Wang
- Electronic health record data mining for artificial intelligence healthcare / Anthony L. Lin, William C. Chen and Julian C. Hong
- Roles of artificial intelligence in wellness, healthy living, and healthy status sensing / Peter Jaeho Cho, Kamika Singh and Jessilyn Dunn
- The growing significance of smartphone apps in data-drivven clinical decision-making: challenges and pitfalls / Iva Halilaj, Yvonka van Wijk, Arthur Jochems and Philippe Lambin
- Artificial intelligence for pathology / Fuyong Xing, Xuhong Zhang and Toby C. Comish
- The potential of deep learning for gastrointestinal endoscopy--a disruptive new technology / Robin Zachariah, Christopher Rombaoa, Jason Samarasena, Duminda Suraweera, Kimberly Wong and William Karnes
- Lessons learnt from harnessing deep learning for real-world clinical applications in ophthalmology: detecting diabetic retinopathy from retinal fundus photographs / Yum Liu, Lu Yang, Sonia Phene and Lily Peng
- Artificial intelligence in radiology / Dakai Jin, Adam P. Harrison, Ling Zhang, Ke Yan, Yirui Wang, Jinzheng Cai, Shun Miao and Le Lu
- Artificial intelligence and interpretations in breast cancer imaging / Hui Li and Maryellen L. Giger
- Prospect and adveristiy of artificial intelligence in urology / Okyaz Eminaga and Joseph C. Liao
- Meaningful incorporation of artificial intelligence for personalized patient management during cancer: quantitative imaging, risk assessment, and therapeutic outcomes / Elisa Warner, Nicholas Wang, Joonsang Lee and Arvind Rao
- Artificial intelligence in oncology / Jean-Emmanuel Bibault, Anita Burgun, Laure Fournier, Andr�e Dekker and Phillippe Lambin
- Artificial intelligence in cardiovascular imaging / Karthik Seetharam and James K. Min
- Artificial intelligence as applied to clinical neurological conditions / Daniel L. Ranti, Aly Al-Amyn Valliani, Anthony Costa and Eric Karl Oermann
- Harnessing the potential of artificial neural networks for pediatric patient management / Jennifer Quon, Michael C. Jin, Jayne Seekins and Kristen W. Yeom
- Artificial intelligence--enabled public health surveillance--from local detection to global epidemic monitoring and control / Daniel Zeng, Zhidong Cao and Daniel B. Neill
- Regulatory, social, ethical, and legal issues of artificial intelligence in medicine / Emily Shearer, Mildred Cho and David Magnus
- Industry perspectives and commercial opportunities of artificial intelligence in medicine / Rebecca Y. Yin and Jeffery B. Alvarez
- Outlook of the future landscape of artificial intelligence in medicine and new challenges / Lei Xing, Daniel S. Kapp, Maryellen L. Giger and James K. Min.