Journal ArticleDOI
Artificial Intelligence in Healthcare
TLDR
Recent breakthroughs in AI technologies and their biomedical applications are outlined, the challenges for further progress in medical AI systems are identified, and the economic, legal and social implications of AI in healthcare are summarized.Abstract:
Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.read more
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Journal ArticleDOI
High-performance medicine: the convergence of human and artificial intelligence
TL;DR: Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen.
Journal Article
Medical Image Computing and Computer-Assisted Intervention
Posted Content
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
TL;DR: ClinicalBERT uncovers high-quality relationships between medical concepts as judged by humans and outperforms baselines on 30-day hospital readmission prediction using both discharge summaries and the first few days of notes in the intensive care unit.
Journal ArticleDOI
Resistance to Medical Artificial Intelligence
TL;DR: These findings make contributions to the psychology of automation and medical decision making, and suggest interventions to increase consumer acceptance of AI in medicine.
Journal ArticleDOI
The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment.
Melissa A. Haendel,Melissa A. Haendel,Christopher G. Chute,Tellen D. Bennett,David Eichmann,Justin Guinney,Warren A. Kibbe,Philip R. O. Payne,Emily R. Pfaff,Peter N. Robinson,Joel H. Saltz,Heidi Spratt,Christine Suver,John Wilbanks,Adam B. Wilcox,Andrew E. Williams,Chunlei Wu,Clair Blacketer,Robert L. Bradford,James J. Cimino,Marshall Clark,Evan W Colmenares,Patricia A Francis,Davera Gabriel,Alexis Graves,Raju Hemadri,Stephanie S Hong,George Hripscak,Dazhi Jiao,Jeffrey G. Klann,Kristin Kostka,Adam M Lee,Harold P Lehmann,Lora Lingrey,Robert T. Miller,Michele I. Morris,Shawn N. Murphy,Karthik Natarajan,Matvey B. Palchuk,Usman Sheikh,Harold R. Solbrig,Shyam Visweswaran,Anita Walden,Anita Walden,Kellie M Walters,Griffin M. Weber,Xiaohan Tanner Zhang,Richard L Zhu,Benjamin Amor,Andrew T Girvin,Amin Manna,Nabeel Qureshi,Michael G. Kurilla,Sam Michael,Lili M Portilla,Joni L Rutter,Christopher P. Austin,Ken R Gersing +57 more
TL;DR: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
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