106 research outputs found

    Societal issues concerning the application of artificial intelligence in medicine

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    Medicine is becoming an increasingly data-centred discipline and, beyond classical statistical approaches, artificial intelligence (AI) and, in particular, machine learning (ML) are attracting much interest for the analysis of medical data. It has been argued that AI is experiencing a fast process of commodification. This characterization correctly reflects the current process of industrialization of AI and its reach into society. Therefore, societal issues related to the use of AI and ML should not be ignored any longer and certainly not in the medical domain. These societal issues may take many forms, but they all entail the design of models from a human-centred perspective, incorporating human-relevant requirements and constraints. In this brief paper, we discuss a number of specific issues affecting the use of AI and ML in medicine, such as fairness, privacy and anonymity, explainability and interpretability, but also some broader societal issues, such as ethics and legislation. We reckon that all of these are relevant aspects to consider in order to achieve the objective of fostering acceptance of AI- and ML-based technologies, as well as to comply with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. Our specific goal here is to reflect on how all these topics affect medical applications of AI and ML. This paper includes some of the contents of the “2nd Meeting of Science and Dialysis: Artificial Intelligence,” organized in the Bellvitge University Hospital, Barcelona, Spain.Peer ReviewedPostprint (author's final draft

    Societal issues concerning the application of artificial intelligence in medicine

    Get PDF
    Medicine is becoming an increasingly data-centred discipline and, beyond classical statistical approaches, artificial intelligence (AI) and, in particular, machine learning (ML) are attracting much interest for the analysis of medical data. It has been argued that AI is experiencing a fast process of commodification. This characterization correctly reflects the current process of industrialization of AI and its reach into society. Therefore, societal issues related to the use of AI and ML should not be ignored any longer and certainly not in the medical domain. These societal issues may take many forms, but they all entail the design of models from a human-centred perspective, incorporating human-relevant requirements and constraints. In this brief paper, we discuss a number of specific issues affecting the use of AI and ML in medicine, such as fairness, privacy and anonymity, explainability and interpretability, but also some broader societal issues, such as ethics and legislation. We reckon that all of these are relevant aspects to consider in order to achieve the objective of fostering acceptance of AI- and ML-based technologies, as well as to comply with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. Our specific goal here is to reflect on how all these topics affect medical applications of AI and ML. This paper includes some of the contents of the “2nd Meeting of Science and Dialysis: Artificial Intelligence,” organized in the Bellvitge University Hospital, Barcelona, Spain.Peer ReviewedPostprint (author's final draft

    Forecasting with Machine Learning

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    For years, people have been forecasting weather patterns, economic and political events, sports outcomes, and more. In this paper we discussed the ways of using machine learning in forecasting, machine learning is a branch of computer science where algorithms learn from data. The fundamental problem for machine learning and time series is the same: to predict new outcomes based on previously known results. Using the suitable technique of machine learning depend on how much data you have, how noisy the data is, and what kind of new features can be derived from the data. But these techniques can improve accuracy and don’t have to be difficult to implement

    Control de procesos por medio de dos configuraciones de redes neuronales. Una revisión

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    En este artículo se explican las estrategias de control de dos configuraciones usando redes neuronales. La primera es denominada control, a partir de un modelo de referencia y utiliza redes de base radial; la segunda es el control predictivo con redes perceptrón multicapa. Asimismo, como introducción al uso de los controladores neuronales, en la sección dos se estudian distintos esquemas de identificación, que también utilizan redes neuronales

    Companion Losses for Deep Neural Networks

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    Modern Deep Neuronal Network backends allow a great flexibility to define network architectures. This allows for multiple outputs with their specific losses which can make them more suitable for particular goals. In this work we shall explore this possibility for classification networks which will combine the categorical cross-entropy loss, typical of softmax probabilistic outputs, the categorical hinge loss, which extends the hinge loss standard on SVMs, and a novel Fisher loss which seeks to concentrate class members near their centroids while keeping these apartThe authors acknowledge financial support from the European Regional Development Fund and the Spanish State Research Agency of the Ministry of Economy, Industry, and Competitiveness under the project PID2019-106827GB-I00. They also thank the UAM–ADIC Chair for Data Science and Machine Learning and gratefully acknowledge the use of the facilities of Centro de Computaci´on Cient´ıfica (CCC) at UA

    Photonic reservoir computing with a network of coupled semiconductor optical amplifiers

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