scholarly journals Artificial intelligence maturity model: a systematic literature review

2021 ◽  
Vol 7 ◽  
pp. e661
Author(s):  
Raghad Baker Sadiq ◽  
Nurhizam Safie ◽  
Abdul Hadi Abd Rahman ◽  
Shidrokh Goudarzi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Approach ◽  
State Of The Art ◽  
Model Development ◽  
Significant Proportion ◽  
Maturity Model ◽  
Methodological Issues ◽  
Maturity Models ◽  
Essential Contribution ◽  
Review State

Organizations in various industries have widely developed the artificial intelligence (AI) maturity model as a systematic approach. This study aims to review state-of-the-art studies related to AI maturity models systematically. It allows a deeper understanding of the methodological issues relevant to maturity models, especially in terms of the objectives, methods employed to develop and validate the models, and the scope and characteristics of maturity model development. Our analysis reveals that most works concentrate on developing maturity models with or without their empirical validation. It shows that the most significant proportion of models were designed for specific domains and purposes. Maturity model development typically uses a bottom-up design approach, and most of the models have a descriptive characteristic. Besides that, maturity grid and continuous representation with five levels are currently trending in maturity model development. Six out of 13 studies (46%) on AI maturity pertain to assess the technology aspect, even in specific domains. It confirms that organizations still require an improvement in their AI capability and in strengthening AI maturity. This review provides an essential contribution to the evolution of organizations using AI to explain the concepts, approaches, and elements of maturity models.

scholarly journals Research IT maturity models for academic health centers: Early development and initial evaluation

1900 ◽  
Vol 2 (5) ◽  
pp. 289-294 ◽  
Author(s):  
Boyd M. Knosp ◽  
William K. Barnett ◽  
Nicholas R. Anderson ◽  
Peter J. Embi
Keyword(s):  
Model Development ◽  
Data Access ◽  
Health Science ◽  
Health Centers ◽  
Academic Health Centers ◽  
Maturity Model ◽  
Maturity Models ◽  
Academic Health ◽  
It Systems ◽  
Technological Advances

AbstractThis paper proposes the creation and application of maturity models to guide institutional strategic investment in research informatics and information technology (research IT) and to provide the ability to measure readiness for clinical and research infrastructure as well as sustainability of expertise. Conducting effective and efficient research in health science increasingly relies upon robust research IT systems and capabilities. Academic health centers are increasing investments in health IT systems to address operational pressures, including rapidly growing data, technological advances, and increasing security and regulatory challenges associated with data access requirements. Current approaches for planning and investment in research IT infrastructure vary across institutions and lack comparable guidance for evaluating investments, resulting in inconsistent approaches to research IT implementation across peer academic health centers as well as uncertainty in linking research IT investments to institutional goals. Maturity models address these issues through coupling the assessment of current organizational state with readiness for deployment of potential research IT investment, which can inform leadership strategy. Pilot work in maturity model development has ranged from using them as a catalyst for engaging medical school IT leaders in planning at a single institution to developing initial maturity indices that have been applied and refined across peer medical schools.

Development of an Industry 4.0 maturity model for the delivery process in supply chains

2018 ◽  
Vol 13 (4) ◽  
pp. 840-883 ◽  
Author(s):  
Björn Asdecker ◽  
Vanessa Felch
Keyword(s):  
Supply Chain ◽  
Industry 4.0 ◽  
Reference Model ◽  
Design Science ◽  
Model Development ◽  
Science Research ◽  
Maturity Model ◽  
Maturity Models ◽  
Content Type ◽  
The Status

Purpose This paper aims to show that current Industry 4.0 maturity models primarily focus on manufacturing processes. Until now, research has been lacking with regard to outbound logistics, that is, the delivery process. This paper develops such a model. Design/methodology/approach Methodologically, this paper is grounded in design science research (DSR) and rigorously follows the model development guidelines presented by De Bruin et al. (2005). This work builds on current maturity models and original empirical research to populate and test the model. Findings The model appears to be applicable to describing the status quo of the digitization efforts in outbound logistics, developing a corporate vision for delivery logistics excellence and providing guidance on the development path. Research limitations/implications Thus far, the model has been applied only for a development stakeholder. For further validation, the authors are currently working on additional case studies to demonstrate the model’s applicability. Practical implications The developed model provides guidance for the digitization of an important value-adding activity in supply chain management: the delivery process. Originality/value To the authors’ knowledge, the proposed model is the first to explicitly consider the delivery process; therefore, it complements available approaches that focus on the manufacturing process. Moreover, the results show that the widely used Supply Chain Operations Reference model can serve as the basis for additional process maturity models.

scholarly journals CHALLENGES OF ADVANCED ANALYTICS MATURITY MODEL DEVELOPMENT

2021 ◽  
Vol 2 ◽  
pp. 88-92
Author(s):  
Santa Lemsa
Keyword(s):  
Decision Making ◽  
Action Plan ◽  
Model Development ◽  
Professional Experience ◽  
Maturity Model ◽  
Process Automation ◽  
Maturity Level ◽  
Maturity Models ◽  
Advanced Analytics ◽  
Automated Decision Making

Significance to understand the advanced analytics ecosystem maturity is increasing caused by constantly growing data volumes and demand for advanced analytics including automated decision making based on data or process automation. The analytics maturity assessment helps to identify strengths and weaknesses of the organization’s analytics ecosystem and can provide detailed action plan to move to the next level. The focus of the paper is to review and analyse analytics maturity models to assess their application as frame to build a new analytics maturity model or replicate with time adjustment any of reviewed models. The literature review and publicly available assessment models provided by analytics sector were used to review and analyse analytics maturity models.  Fifteen models were reviewed and four of them analysed by twelve characteristics. Summary of four models includes analytics maturity levels, domains, accessibility of questionnaire, discloser of maturity level detection and authors assessment of several characteristics. Comprehensive descriptions of analytics maturity levels were available for many models. Solid recommendation sets for each maturity level provided for the most disclosed models. One of the most important components, approach to detect specific maturity level, was not transparent or disclosed with limitations. However, it is possible to develop a new model or replicate in some extent based on models reviewed in this paper, but it requires extensive professional experience in advanced analytics and related functions. 

Establishment of a maturity model to assess the development of industrial AI in smart manufacturing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wenting Chen ◽  
Caihua Liu ◽  
Fei Xing ◽  
Guochao Peng ◽  
Xi Yang
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Firms ◽  
Smart Manufacturing ◽  
Maturity Model ◽  
Structured Interviews ◽  
Maturity Models ◽  
Content Type ◽  
Domain Experts ◽  
Road Map ◽  
Industrial Context

PurposeThe benefits of artificial intelligence (AI) related technologies for manufacturing firms are well recognized, however, there is a lack of industrial AI (I-AI) maturity models to enable companies to understand where they are and plan where they should go. The purpose of this study is to propose a comprehensive maturity model in order to help manufacturing firms assess their performance in the I-AI journey, shed lights on future improvement, and eventually realize their smart manufacturing visions.Design/methodology/approachThis study is based on (1) a systematic review of literature on assessing I-AI-related technologies to identify relevant measured indicators in the maturity model, and (2) semi-structured interviews with domain experts to determine maturity levels of the established model.FindingsThe I-AI maturity model developed in this study includes two main dimensions, namely “Industry” and “Artificial Intelligence”, together with 12 first-level indicators and 35 second-level indicators under these dimensions. The maturity levels are divided into five types: planning level, specification level, integration level, optimization level, and leading level.Originality/valueThe maturity model integrates indicators that can be used to assess AI-related technologies and extend the existing maturity models of smart manufacturing by adding specific technical and nontechnical capabilities of these technologies applied in the industrial context. The integration of the industry and artificial intelligence dimensions with the maturity levels shows a road map to improve the capability of applying AI-related technologies throughout the product lifecycle for achieving smart manufacturing.

Effects of Psychotherapy on Brain Activation Patterns in Anxiety Disorders

2016 ◽  
Vol 224 (2) ◽  
pp. 62-70 ◽  
Author(s):  
Thomas Straube
Keyword(s):  
Anxiety Disorders ◽  
Neuronal Plasticity ◽  
Functional Neuroimaging ◽  
State Of The Art ◽  
Methodological Issues ◽  
Future Directions ◽  
Activation Patterns ◽  
New Learning ◽  
Analytical Strategies ◽  
Sample Characteristics

Abstract. Psychotherapy is an effective treatment for most mental disorders, including anxiety disorders. Successful psychotherapy implies new learning experiences and therefore neural alterations. With the increasing availability of functional neuroimaging methods, it has become possible to investigate psychotherapeutically induced neuronal plasticity across the whole brain in controlled studies. However, the detectable effects strongly depend on neuroscientific methods, experimental paradigms, analytical strategies, and sample characteristics. This article summarizes the state of the art, discusses current theoretical and methodological issues, and suggests future directions of the research on the neurobiology of psychotherapy in anxiety disorders.

scholarly journals Clinically applicable artificial intelligence system for dental diagnosis with CBCT

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Matvey Ezhov ◽  
Maxim Gusarev ◽  
Maria Golitsyna ◽  
Julian M. Yates ◽  
Evgeny Kushnerev ◽  
...  
Keyword(s):  
State Of The Art ◽  
Model Development ◽  
Clinical Effectiveness ◽  
Anatomical Landmarks ◽  
Periapical Lesion ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Significant Difference ◽  
Diagnostic Capabilities ◽  
Lesion Localization

AbstractIn this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinical setting. The system consists of 5 modules: ROI-localization-module (segmentation of teeth and jaws), tooth-localization and numeration-module, periodontitis-module, caries-localization-module, and periapical-lesion-localization-module. These modules use CNN based on state-of-the-art architectures. In total, 1346 CBCT scans were used to train the modules. After annotation and model development, the AI system was tested for diagnostic capabilities of the Diagnocat AI system. 24 dentists participated in the clinical evaluation of the system. 30 CBCT scans were examined by two groups of dentists, where one group was aided by Diagnocat and the other was unaided. The results for the overall sensitivity and specificity for aided and unaided groups were calculated as an aggregate of all conditions. The sensitivity values for aided and unaided groups were 0.8537 and 0.7672 while specificity was 0.9672 and 0.9616 respectively. There was a statistically significant difference between the groups (p = 0.032). This study showed that the proposed AI system significantly improved the diagnostic capabilities of dentists.

scholarly journals Gemini Principles-Based Digital Twin Maturity Model for Asset Management

2021 ◽  
Vol 13 (15) ◽  
pp. 8224
Author(s):  
Long Chen ◽  
Xiang Xie ◽  
Qiuchen Lu ◽  
Ajith Kumar Parlikad ◽  
Michael Pitt ◽  
...  
Keyword(s):  
Case Studies ◽  
Asset Management ◽  
Questionnaire Survey ◽  
New Technologies ◽  
Maturity Model ◽  
Maturity Models ◽  
Digital Twin ◽  
Project Level

Various maturity models have been developed for understanding the diffusion and implementation of new technologies/approaches. However, we find that existing maturity models fail to understand the implementation of emerging digital twin technique comprehensively and quantitatively. This research aims to develop an innovative maturity model for measuring digital twin maturity for asset management. This model is established based on Gemini Principles to form a systematic view of digital twin development and implementation. Within this maturity model, three main dimensions consisting of nine sub-dimensions have been defined firstly, which were further articulated by 27 rubrics. Then, a questionnaire survey with 40 experts involved is designed and conducted to examine these rubrics. This model is finally illustrated and validated by two case studies in Shanghai and Cambridge. The results show that the digital twin maturity model is effective to qualitatively evaluate and compare the maturity of digital twin implementation at the project level. It can also initiate the roadmap for improving the performance of digital twin supported asset management.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

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