The healthcare industry makes one of the main components of the productive forces of the state. Therefore, the well-being and welfare of the entire society in the future depend on its thriving development. Despite significant accumulated knowledge in medicine, there are still some white spots that are difficult to analyze and predict. The use of artificial intelligence and neural networks in healthcare can significantly expand the analytical apparatus and radically change the existing approaches to scientific research. This article discusses the results of the practical application of artificial intelligence and artificial neural networks in healthcare. The research aims to demonstrate the prospects and advantages of using these information technologies in medicine; identify problems in the implementation of AI technologies in medical practice and offer possible solutions to some of them. The authors provide a comprehensive literature review on the issues of artificial intelligence and neural networks, consider successful examples of the AI use in pharmacology, forecasting, and research of various types of diseases, including cardiovascular system, dermatology, and oncology. A significant part of the research is devoted to ethical and legal concerns, as well as problems associated with the practical use of artificial intelligence. As a result of the research, the authors suggest the models of the IT architecture of a medical information system and data flows, based on the TOGAF standard.
Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to
weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of ma-
chine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models
for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty
with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty.
Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for
both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential
neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type
and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling stan-
dard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the
predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the
context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models.
The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly
extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science
modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine
Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS)
(MILES-GUESS) (https://github.com/ai2es/miles-guess), that enables users to train and evaluate both evidential
В статье рассмотрено влияние нейросетей на интеллектуальную собственность в маркетинговых исследованиях. Автор утверждает, что растущее использование искусственного интеллекта и машинного обучения в маркетинговых исследованиях создает
новые проблемы для защиты прав интеллектуальной собственности. Статья дает представление о правовых тенденциях, связанных с использованием технологий в маркетинговых исследованиях, которые были проанализированы с помощью количественного контент-анализа и метода кейсов (case studies). Статья представляет собой ценный ресурс для исследователей, практиков и политиков, заинтересованных в проблемах
интеллектуальной собственности и искусственного интеллекта.