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Title Artificial intelligence in medicine technical basis and clinical applications / edited by Lei Xing, Maryellen L. Giger and James K. Min
Published London, United Kingdom : Academic Press, [2021]
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Description 1 online resource
Contents 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, André 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
Summary 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 at a breakneck speed. The field of medicine, with its availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI. AI also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches
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 skepticism. This book can help clinicians understand how AI works, thus helping them develop a more informed appreciation for AI's strengths and limitations. Only then can clinicians harness AI's computational power to streamline workflow and improve patient care. On the other hand, this book can profide computer scientists and data analysts with clinical insight to help focus their efforts into developing practical medical applications. This book provides vital background knowledge to help bring these two groups together, facilitating a streamlined dialog to yield productive collaborative solutions in the field of medicine. -- P. 4 of cover
Notes Includes index
Bibliography Includes bibliographical references and index
Subject Artificial intelligence -- Medical applications.
Artificial Intelligence.
Artificial intelligence -- Medical applications
Form Electronic book
Author Giger, Maryellen Lissak, 1956-
Min, James K.
Xing, Lei.
ISBN 0128212586