Artificial neural networks in medical diagnosis

By Filippo Amato, Alberto López, E. Peña-Méndez, P. Vaňhara, A. Hampl, J. Havel. 2013

534 citations recorded by the source corpus.

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Artificial intelligence-enabled healthcare delivery (2018, background): “Artificial neural networks can tolerate a certain amount of noise in the data but sometimes this can have an impact on the predictions.(33) Therefore, it is important to combine the expertise and experience of clinicians with the discriminative power of the artificial neural networks.”
A Decade Survey of Content Based Image Retrieval Using Deep Learning (2020, methodology): “Based on the type of data to be processed, different architectures came into existence such as Artificial Neural Network (ANN)/ Multilayer Perceptron (MLP) for 1-D data [37], [38], [39], Convolutional Neural Networks (CNN) for image data [40], [41], [42], and Reurrent Neural Networks (RNN) for time-”
An abstract domain for certifying neural networks (2019, background): “Over the last few years, deep neural networks have become increasingly popular and have now started penetrating safety critical domains such as autonomous driving [Bojarski et al. 2016] and medical diagnosis [Amato et al. 2013] where they are often relied upon for making important decisions.”
Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review (2019, background): “Although the recently promising self-learning abilities of AI may lead to additional prospects [22], the viability of such diagnostic processes is inevitably determined by human experts through cumulative clinical experience [23,24].”