scholarly journals Structuring the Quest for Strategic Alignment of Artificial Intelligence (AI): A Taxonomy of the Organizational Business Value of AI Use Cases

2022 ◽  
Author(s):  
Christian Engel ◽  
Julius Schulze Buschhoff ◽  
Philipp Ebel
Keyword(s):  
Artificial Intelligence ◽  
Strategic Alignment ◽  
Business Value ◽  
Use Cases

scholarly journals AI MSK clinical applications: spine imaging

2021 ◽  
Author(s):  
Florian A. Huber ◽  
Roman Guggenberger
Keyword(s):  
Artificial Intelligence ◽  
Clinical Application ◽  
Value Chain ◽  
Clinical Applications ◽  
Status Quo ◽  
Use Cases ◽  
Musculoskeletal Imaging ◽  
Future Perspective ◽  
Spine Imaging ◽  
The Status

AbstractRecent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions.

scholarly journals Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ania Syrowatka ◽  
Masha Kuznetsova ◽  
Ava Alsubai ◽  
Adam L. Beckman ◽  
Paul A. Bain ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Real Time ◽  
Scoping Review ◽  
Grey Literature ◽  
Use Cases ◽  
Outbreak Detection ◽  
Response To Treatment ◽  
Pandemic Preparedness ◽  
Modeling Approaches

AbstractArtificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

An Integration Approach of Educational Artificial Intelligence (AI) Use Cases Into a Demonstration Factory

2021 ◽  
Author(s):  
Tobias Eusterwiemann ◽  
Florian Eiling ◽  
Isabelle Gauger ◽  
Andreas Bildstein
Keyword(s):  
Artificial Intelligence ◽  
Use Cases ◽  
Integration Approach

Network Security Policy Automation

2022 ◽  
pp. 83-112
Author(s):  
Myo Zarny ◽  
Meng Xu ◽  
Yi Sun
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Network Security ◽  
Security Policy ◽  
Learning Technologies ◽  
Use Cases ◽  
Hybrid Cloud ◽  
Cloud Environments ◽  
Enterprise Security

Network security policy automation enables enterprise security teams to keep pace with increasingly dynamic changes in on-premises and public/hybrid cloud environments. This chapter discusses the most common use cases for policy automation in the enterprise, and new automation methodologies to address them by taking the reader step-by-step through sample use cases. It also looks into how emerging automation solutions are using big data, artificial intelligence, and machine learning technologies to further accelerate network security policy automation and improve application and network security in the process.

How to leverage artificial intelligence to meet your diversity goals

2019 ◽  
Vol 18 (2) ◽  
pp. 62-65
Author(s):  
Tammy Cohen
Keyword(s):  
Artificial Intelligence ◽  
Design Methodology ◽  
New Technologies ◽  
Use Cases ◽  
Content Type

Purpose This paper aims to provide insights into how artificial intelligence can be used to eliminate bias in employee screening. Design/methodology/approach Industry use cases and expert analytics were used in conducting this paper. Findings Artificial intelligence if used correctly can help to build more diverse and inclusive teams and eliminate bias. Originality/value This paper shows how leveraging new technologies such as AI can cut down on bias across employee screenings.

scholarly journals Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Interdisciplinary Approach ◽  
Comprehensive Analysis ◽  
The State ◽  
Use Cases

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

Business viability of potential software projects using artificial neural network

2020 ◽  
Vol 26 (1) ◽  
Author(s):  
O. Okolo ◽  
B.Y Baha
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Intelligent Systems ◽  
Project Selection ◽  
Business Value ◽  
Synthetic Approach ◽  
Software Project ◽  
Unseen Data ◽  
Artificial Neural

Selection of a software project is a critical decision. This selection involves prediction to ascertain a project that provides the best business value to the organization. The process of selection is carefully undertaken to optimize scarce resources available, which makes it impossible to simultaneously invest in all business ideas and systems. The current traditional method of software selection does not consider risk factors among the many variables necessary to predict a project that could provide the best business value. More so, the current method such as an artificial intelligence approach, where project managers use more robust models to make predictions have not received the needed attention in developing models for software project selection. This research applied a branch of Artificial Intelligence called Artificial Neural Network to classify projects into three levels. The research designed an artificial neural network of four inputs, one hidden layer with twenty-seven (27) neurons, and three outputs. Keras, a python deep learning library that runs on a theano background was used to implement the model. This research used a secondary dataset, which was enhanced by the synthetic approach, to make the required data features needed in machine learning applications. Backpropagation Algorithm enabled the model to train and learn from the data, and K-fold cross-validation was used to measure the accuracy of the model on unseen data. The results of the simulation showed that the model performed up to 98.67% accuracy with a standard deviation of 2.6% performance on unseen data. The research concludes that using the artificial neural network for software project selection unleashes a new vista of opportunities in artificial i ntelligence where intelligent systems are developed based on robust models from data forproject selection.Keywords: Artificial Neural Network, Project selection, Machine LearningVol. 26, No. 1, June 2019

AI risks being misunderstood in the workplace

2019 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Business Performance ◽  
Use Cases ◽  
Positive Outcomes ◽  
Negative Outcomes ◽  
Content Type ◽  
Effective Use

Subject AI in the workplace. Significance Positive use cases for artificial intelligence (AI) systems are rising, but misuse means the number of negative examples is also rising, drawing attention to how to regulate it. Impacts Effective use of AI within appropriate contexts will improve business performance in many sectors. Current law is not suitable for some emerging forms of AI, but to gain competitiveness, some regions may prioritise efficiency over safety. Misuse of AI will become a major source of negative outcomes at work, likely outweighing the positive outcomes. Future uses of AI will become increasingly hard to manage or regulate. Firms expanding their 'ethical' activities and then arguing that more regulation would limit them will raise fears of ‘ethical washing’.

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