Banegas-Luna Antonio Jesús, Peña-García Jorge, Iftene Adrian, Guadagni Fiorella, Ferroni Patrizia, Scarpato Noemi, Zanzotto Fabio Massimo, Bueno-Crespo Andrés, Pérez-Sánchez Horacio
Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain.
Faculty of Computer Science, Universitatea Alexandru Ioan Cuza (UAIC), 700505 Jashi, Romania.
Int J Mol Sci. 2021 Apr 22;22(9):4394. doi: 10.3390/ijms22094394.
Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine-specifically, to cancer research-and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors' predictive capacity and achieve individualised therapies in the near future.
人工智能正在取得惊人的成果，医学是其最青睐的领域之一。机器学习，尤其是深度神经网络，是这场革命的幕后推手。医学领域最具挑战性的目标之一是癌症诊断和治疗，但要开启这场革命，软件工具需要进行调整以满足新的需求。从这个意义上说，学习工具正变得越来越普遍，但要能够在日常工作中协助医生，全面理解模型如何被解释至关重要。在本次综述中，我们分析了当前应用于医学领域——特别是癌症研究——的机器学习模型和其他计算机模拟工具，并讨论了它们的可解释性、性能以及所使用的输入数据。人工神经网络（ANN）、逻辑回归（LR）和支持向量机（SVM）被认为是首选模型。此外，在图形处理单元（GPU）和高性能计算（HPC）基础设施快速发展的支持下，卷积神经网络（CNN）在可行的图像处理中变得越来越重要。然而，机器学习预测的可解释性，以便医生能够理解、信任并从中获得对临床实践有用的见解，仍然很少被考虑，这是一个需要改进的因素，以提高医生的预测能力，并在不久的将来实现个性化治疗。
