A guide to deep learning in healthcare

By Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark A. DePristo, Katherine Chou, Claire Cui, Greg S. Corrado, Sebastian Thrun, Joseph Dean. 2019

4,405 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging, Medical & Health AI

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Citations in the corpus

Toward Causal Representation Learning (2021, background): “One example is personalized medicine, where we may wish to build a model of a patient health state through a multitude of data sources, such as electronic health records and genetic information [66], [109].”
Towards Causal Representation Learning (2021, background): “One example is personalized medicine, where we may wish to build a model of a patient health state through a multitude of data sources, like electronic health records and genetic information [65, 108].”
A Reinforcement Learning–Based Method for Management of Type 1 Diabetes: Exploratory Study (2019, neutral): “In health care, using medical image and treatment regimen–related information from historical medical data, RL was used for cancer prediction, diagnosis, and prognosis [32,33].”
Reinforcement Learning in Healthcare: A Survey (2019, methodology): “methods and machine learning approaches in a variety of healthcare applications [5], [6], [7], [8], [9].”
Shifting machine learning for healthcare from development to deployment and from models to data (2022, background): “Harnessing natural language processing (NLP)—the automated understanding of text—has been a long-standing goal for ML in healthcare 1,16,17 .”
Revisiting Recursive Least Squares for Training Deep Neural Networks (2021, background): “Over the past decade, DL has succeeded in many ﬁelds, including computer vision [3], speech recognition [4], natural language processing [5], recommender system [6], healthcare [7], etc.”
BEHRT: Transformer for Electronic Health Records (2019, background): “ng Author for this work. arXiv:1907.09538v1 [cs.LG] 22 Jul 2019 A PREPRINT - JULY 24, 2019 factors from just a retinal fundus photograph; and the list continues (more examples can be found in [3] and [4]). A key contributing factor to this success, in addition to the developments in DL algorithms, w”
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging (2019, background): “Deep learning systems have shown remarkable promise in medical image analysis, often claiming performance rivaling that of human experts [13].”
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks (2020, background): “The need for protecting input privacy in machine learning (ML) is growing rapidly in many areas such as health care [1], autonomous vehicles [2], business [3], communication [4] and etc.”
Adversarial Robustness with Semi-Infinite Constrained Learning (2021, background): “Learning is at the core of many modern information systems, with wide-ranging applications in clinical research [1–4], smart grids [5–7], and robotics [8–10].”
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians (2019, background): “Existing literature in the machine learning field provides relatively successful methods to train such mathematical models that learn useful knowledge from data [9-11].”