Deep Learning-A Technology With the Potential to Transform Health Care.

By Geoffrey E. Hinton. 2018

597 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Information Management, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “This is illustrated by the tremendous amounts of high-impact publications in top-journals dealing with deep learning in medical imaging (for example [16,20,29,31,39,89,136,139,161,272,284], all published in 2018).”
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, background): “Its rapid development in recent times is mostly due to a massive inﬂux of data, advances in computing power and developments in learning algorithms that enabled the construction of multi-layer (more than two) networks ( Hinton, 2018; Voulodimos et al., 2018 ).”
AI in Health: State of the Art, Challenges, and Future Directions (2019, methodology): “DL approaches [1, 3] address this problem by adopting an end-to-end learning architecture, using raw patient data as an input and mapping it to outcomes through multiple layers of nonlinear processing units (i.”
Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey (2020, background): “It is only more recently that an increasingly circumspect view of AI adaptation in medical imaging has gained acceptance [13], including by some of the same thought leaders that originally predicted the rapid demise of radiologists [15].”
Ensuring Fairness in Machine Learning to Advance Health Equity (2018, background): “However, health equity will not be attained unless we purposely design our health and social systems, which increasingly will be infused with machine learning (12), to achieve this goal.”
Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment (2020, methodology): “However, sophisticated new ML models (e.g., those used in “deep learning” [42,43]) are well suited to learn from the complex and heterogeneous kinds of data that are generated from current clinical care, such as medical notes entered by doctors, medical images, continuous monitoring data from…”
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, methodology): “Deep learning to transform healthcare (48)”
Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020, background): “The imaging ﬁ ndings on chest X-rays consistent with pneumonia, despite having imaging features that can generally differentiate alternative diagnoses, are nonspeci ﬁ c and accurate diagnosis requires the context of clinical and laboratory data.”
Machine Learning in Drug Discovery: A Review (2021, unknown): “2018; Hinton 2018).”
Neural Networks Fail to Learn Periodic Functions and How to Fix It (2020, background): “such as very limited (only 25 points for training) and insufﬁcient measurement taken over irregular intervals, when applying deep learning to real problems such as medical or physiological prediction [18, 33]. In particular, we have a dataset where data points from certain period in a day is missing”