scholarly journals Using Artificial Intelligence to improve prediction and prevention of violence

2021 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Prevention Of Violence ◽  
Prediction And Prevention

scholarly journals Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

2021 ◽  
Vol 22 (19) ◽  
pp. 10291
Author(s):  
Annie M. Westerlund ◽  
Johann S. Hawe ◽  
Matthias Heinig ◽  
Heribert Schunkert
Keyword(s):  
Artificial Intelligence ◽  
Risk Prediction ◽  
Cardiovascular Events ◽  
Regulatory Networks ◽  
Recurrent Events ◽  
Large Scale ◽  
Treatment Strategies ◽  
Molecular Data ◽  
Holistic View ◽  
Prediction And Prevention

Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.

scholarly journals The impact of artificial intelligence along the insurance value chain and on the insurability of risks

2021 ◽  
Author(s):  
Martin Eling ◽  
Davide Nuessle ◽  
Julian Staubli
Keyword(s):  
Artificial Intelligence ◽  
Value Chain ◽  
Insurance Coverage ◽  
Low Frequency ◽  
Insurance Companies ◽  
Future Research ◽  
Data Set ◽  
Insurance Sector ◽  
Prediction And Prevention ◽  
The Impact

AbstractBased on a data set of 91 papers and 22 industry studies, we analyse the impact of artificial intelligence on the insurance sector using Porter’s (1985) value chain and Berliner’s (1982) insurability criteria. Additionally, we present future research directions, from both the academic and practitioner points of view. The results illustrate that both cost efficiencies and new revenue streams can be realised, as the insurance business model will shift from loss compensation to loss prediction and prevention. Moreover, we identify two possible developments with respect to the insurability of risks. The first is that the application of artificial intelligence by insurance companies might allow for a more accurate prediction of loss probabilities, thus reducing one of the industry’s most inherent problems, namely asymmetric information. The second development is that artificial intelligence might change the risk landscape significantly by transforming some risks from low-severity/high-frequency to high-severity/low-frequency. This requires insurance companies to rethink traditional insurance coverage and design adequate insurance products.

scholarly journals Artificial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children

2020 ◽  
Vol 3 ◽  
Author(s):  
Xanthe Hunt ◽  
Mark Tomlinson ◽  
Siham Sikander ◽  
Sarah Skeen ◽  
Marguerite Marlow ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Violence Against Children ◽  
Prevention Of Violence

Artificial intelligence and crime: A primer for criminologists

2020 ◽  
pp. 174165902091743
Author(s):  
Keith J Hayward ◽  
Matthijs M Maas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Criminal Justice ◽  
Potential Application ◽  
Scaling Up ◽  
General Review ◽  
The Third ◽  
Prediction And Prevention

This article introduces the concept of Artificial Intelligence (AI) to a criminological audience. After a general review of the phenomenon (including brief explanations of important cognate fields such as ‘machine learning’, ‘deep learning’, and ‘reinforcement learning’), the paper then turns to the potential application of AI by criminals, including what we term here ‘crimes with AI’, ‘crimes against AI’, and ‘crimes by AI’. In these sections, our aim is to highlight AI’s potential as a criminogenic phenomenon, both in terms of scaling up existing crimes and facilitating new digital transgressions. In the third part of the article, we turn our attention to the main ways the AI paradigm is transforming policing, surveillance, and criminal justice practices via diffuse monitoring modalities based on prediction and prevention. Throughout the paper, we deploy an array of programmatic examples which, collectively, we hope will serve as a useful AI primer for criminologists interested in the ‘tech-crime nexus’.

scholarly journals Artificial Intelligence in Crime Counteraction, Prediction, Prevention and Evolution

2018 ◽  
Vol 12 (6) ◽  
pp. 753-766 ◽  
Author(s):  
Alexander Sukhodolov ◽  
Anna Bychkova
Keyword(s):  
Artificial Intelligence ◽  
Law Enforcement ◽  
Crime Prevention ◽  
Criminal Behavior ◽  
Predictive Analytics ◽  
Facial Recognition ◽  
Prediction System ◽  
Software Complex ◽  
Crime Prediction ◽  
Prediction And Prevention

Crime prediction, prevention and counteraction with the use of modern technologies should, according to the authors, become a priority task for the state, along with the development of economy, education, medicine and the enhancement of defense capacity. The article describes the concepts of «artificial intelligence», «machine learning», «big data», «deep learning», «neural networks» from the standpoint of how they are used both by criminals and by law enforcement bodies and courts. The authors examine the application of technologies which use artificial intelligence, hi tech crime (fishing, drones, fake information, bots, and so on). They outline modern software solutions based on artificial intelligence and aimed at counteracting crime: software that analyzes big volumes of data, processing of stream videos, facial recognition, contextual searching platforms, etc. The authors also describe the existing resources for predictive analytics (in particular, inter-agency experimental software «Artificial Intelligence in Police Work and Investigation of Criminal Offences»; software for recognizing people based on fragments of their tattoos; facial recognition of people after plastic surgeries in pictures and stream videos, with the generation of variants of their original appearance; platform of contextual intelligence Nigel; system Mayhem and others) and how they can be used to predict both crimes in general and individual criminal behavior. The authors also outline ethical dilemmas connected with legal decisions made by artificial intelligence regarding specific people. They present examples of using artificial intelligence for crime prevention (software COMPAS, criminal community’s psychometric prediction system, Harm Assessment Risk Tool, analytical software complex CEG, crime prediction system PredPol, ePOOLICE system, Palantir software, Russian system «Artificial intelligence»). They also outline the indicators of the early crime prevention system: indicators of matching, lagging, cyclical and counter-cyclical indicators. The authors state that Russia is lagging behind other countries in its use of artificial intelligence in law enforcement and suggest adopting the Modern Strategy of Crime Counteraction, Prediction and Prevention. Possible directions of this strategy are described.

scholarly journals Artificial Intelligence for Diabetes Management and Decision Support: Literature Review (Preprint)

2018 ◽  
Author(s):  
Ivan Contreras ◽  
Josep Vehi
Keyword(s):  
Artificial Intelligence ◽  
Chronic Diseases ◽  
Diabetes Management ◽  
Daily Practice ◽  
Self Management ◽  
Review Of The Literature ◽  
Research Activity ◽  
Artificial Intelligence Methods ◽  
Prediction And Prevention

BACKGROUND Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. OBJECTIVE The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges. METHODS A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review. RESULTS We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. CONCLUSIONS We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

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