scholarly journals Artificial Intelligence as a Decision Making Tool for Military Leaders

2021 ◽  
Vol 26 (4) ◽  
pp. 269-273
Author(s):  
Alexandru Baboş
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Military Leaders ◽  
Future Leaders ◽  
The Impact ◽  
Decision Making Tool

Abstract Capable of processing the volume, variety, accuracy and speed of data at rates and precision impossible for humans to achieve, artificial intelligence-enabled systems offer great potential in supporting the decision making. However, the impact of the use of AI will not be solely for future leaders, but will also affect those they lead. This article means to highlight these possible effects on leaders, by presenting some important connections between AI and leadership.

scholarly journals Stochastic Fuzzy Algorithms for Impairment of Assets Management

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Marcel Ioan Bolos ◽  
Victoria Bogdan ◽  
Ioana Alexandra Bradea ◽  
Claudia Diana Sabau Popa ◽  
Dorina Nicoleta Popa
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Fuzzy Numbers ◽  
Cash Flows ◽  
The Other ◽  
Dual Purpose ◽  
Fuzzy Algorithms ◽  
Tangible Assets ◽  
The Impact

The present paper aims to analyze the impairment of tangible assets with the help of artificial intelligence. Stochastic fuzzy numbers have been introduced with a dual purpose: on one hand to estimate the cash flows generated by tangible assets exploitation and, on the other hand, to ensure the value ranges stratifications that define these cash flows. Estimation of cash flows using stochastic fuzzy numbers was based on cash flows generated by tangible assets in previous periods of operation. Also, based on the Lagrange multipliers, were introduced: the objective function of minimizing the standard deviations from the recorded value of the cash flows generated by the tangible assets, as well as the constraints caused by the impairment of tangible assets identification according to which the cash flows values must be equal to the annual value of the invested capital. Within the determination of the impairment value and stratification of the value ranges determined by the cash flows using stochastic fuzzy numbers, the impairment of assets risk was identified. Information provided by impairment of assets but also the impairment risks, is the basis of the decision-making measures taken to mitigate the impact of accumulated impairment losses on company’s financial performance.

scholarly journals Using building adaptation to deliver sustainability in Australia

2009 ◽  
Vol 27 (1) ◽  
pp. 46-61 ◽  
Author(s):  
Sara J. Wilkinson ◽  
Kimberley James ◽  
Richard Reed
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Office Building ◽  
Building Stock ◽  
Policy Makers ◽  
Content Type ◽  
Carbon Neutrality ◽  
Building Adaptation ◽  
The Impact ◽  
Decision Making Tool

PurposeThis paper seeks to establish the rationale for existing office building adaptation within Melbourne, Australia, as the city strives to become carbon neutral by 2020. The problems faced by policy makers to determine which buildings have the optimum adaptation potential are to be identified and discussed.Design/methodology/approachThis research adopts the approach of creating a database of all the buildings in the Melbourne CBD including details of physical, social, economic and technological attributes. This approach will determine whether relationships exist between attributes and the frequency of building adaptation or whether triggers to adaptation can be determined.FindingsThis research provided evidence that a much faster rate of office building adaptation is necessary to meet the targets already set for carbon neutrality. The findings demonstrate that a retrospective comprehensive examination of previous adaptation in the CBD is a unique and original approach to determining the building characteristics associated with adaptation and whether triggers can be identified based on previous practices. The implication is that a decision‐making tool should be developed to allow policy makers to target sectors of the office building stock to deliver carbon neutrality within the 2020 timeframe.Practical implicationsDrastic reductions in greenhouse gas emissions are required to mitigate global warming and climate change and all stakeholders should be looking at ways of reducing emissions from existing stock.Originality/valueThis paper adds to the existing body of knowledge by raising awareness of the way in which the adaptation of large amounts of existing stock can be fast tracked to mitigate the impact of climate change and warming associated with the built environment, and in addition it establishes a framework for a decision‐making tool for policy makers.

scholarly journals Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction

2019 ◽  
Vol 33 (2) ◽  
pp. 31-50 ◽  
Author(s):  
Ajay Agrawal ◽  
Joshua S. Gans ◽  
Avi Goldfarb
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Labor Market ◽  
Market Impact ◽  
Recent Advances ◽  
The Impact ◽  
Decision Tasks

Recent advances in artificial intelligence are primarily driven by machine learning, a prediction technology. Prediction is useful because it is an input into decision-making. In order to appreciate the impact of artificial intelligence on jobs, it is important to understand the relative roles of prediction and decision tasks. We describe and provide examples of how artificial intelligence will affect labor, emphasizing differences between when the automation of prediction leads to automating decisions versus enhancing decision-making by humans.

scholarly journals Group Decision-Making Based on Artificial Intelligence: A Bibliometric Analysis

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1566
Author(s):  
Ruben Heradio ◽  
David Fernandez-Amoros ◽  
Cristina Cerrada ◽  
Manuel J. Cobo
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Bibliometric Analysis ◽  
Group Decision Making ◽  
Group Decision ◽  
Single Person ◽  
Group Members ◽  
Linguistic Approaches ◽  
Fuzzy Linguistic ◽  
The Impact

Decisions concerning crucial and complicated problems are seldom made by a single person. Instead, they require the cooperation of a group of experts in which each participant has their own individual opinions, motivations, background, and interests regarding the existing alternatives. In the last 30 years, much research has been undertaken to provide automated assistance to reach a consensual solution supported by most of the group members. Artificial intelligence techniques are commonly applied to tackle critical group decision-making difficulties. For instance, experts’ preferences are often vague and imprecise; hence, their opinions are combined using fuzzy linguistic approaches. This paper reports a bibliometric analysis of the ample literature published in this regard. In particular, our analysis: (i) shows the impact and upswing publication trend on this topic; (ii) identifies the most productive authors, institutions, and countries; (iii) discusses authors’ and journals’ productivity patterns; and (iv) recognizes the most relevant research topics and how the interest on them has evolved over the years.

scholarly journals Use of Artificial Intelligence in Smart Cities for Smart Decision-Making: A Social Innovation Perspective

2022 ◽  
Vol 14 (2) ◽  
pp. 620
Author(s):  
Syed Asad A. Bokhari ◽  
Seunghwan Myeong
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Social Innovation ◽  
Survey Design ◽  
Smart Cities ◽  
Mediating Effect ◽  
Decision Making Process ◽  
Science Literature ◽  
Cross Sectional ◽  
The Impact

The goal of this study is to investigate the direct and indirect relationships that exist between artificial intelligence (AI), social innovation (SI), and smart decision-making (SDM). This study used a survey design and collected cross-sectional data from South Korea and Pakistan using survey questionnaires. Four hundred sixty respondents from the public and private sectors were obtained and empirically analyzed using SPSS multiple regression. The study discovered a strong and positive mediating effect of SI between the relationship of AI and SDM, as predicted. Previous researchers have investigated some of the factors that influence the decision-making process. This study adds to the social science literature by examining the impact of a mediating factor on decision-making. The findings of this study will contribute to the local government in building smart cities such that the factor of social innovations should be involved in the decision-making process because smart decision-making would share such collected data with entrepreneurs, businesses, and industries and would benefit society and all relevant stakeholders, including such social innovators.

scholarly journals The Impact of Artificial Intelligence on Health Equity in Oncology: A Scoping Review

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4934-4934
Author(s):  
Paul Istasy ◽  
Wen Shen Lee ◽  
Alla Iansavitchene ◽  
Ross Upshur ◽  
Bekim Sadikovic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Health Equity ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Ethical Issues ◽  
Clinical Decision ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
The Impact

Abstract Introduction: The expanding use of Artificial Intelligence (AI) in hematology and oncology research and practice creates an urgent need to consider the potential impact of these technologies on health equity at both local and global levels. Fairness and equity are issues of growing concern in AI ethics, raising problems ranging from bias in datasets and algorithms to disparities in access to technology. The impact of AI on health equity in oncology, however, remains underexplored. We conducted a scoping review to characterize, evaluate, and identify gaps in the existing literature on AI applications in oncology and their implications for health equity in cancer care. Methodology: We performed a systematic literature search of MEDLINE (Ovid) and EMBASE from January 1, 2000 to March 28, 2021 using key terms for AI, health equity, and cancer. Our search was restricted to English language abstracts with no restrictions by publication type. Two reviewers screened a total of 9519 abstracts, and 321 met inclusion criteria for full-text review. 247 were included in the final analysis after assessment by three reviewers. Studies were analysed descriptively, by location, type of cancer and AI application, as well as thematically, based on issues pertaining to health equity in oncology. Results: Of the 247 studies included in our analysis, 150 (60.7%) were based in North America, 57 (23.0%) in Asia, 29 (11.7%) in Europe, 5 (2.1%) in Central/South America, 4 (1.6%) in Oceania, and 2 (0.9%) in Africa. 71 (28.6%) were reviews and commentaries, and 176 were (71.3%) clinical studies. 25 (10.1%) focused on AI applications in screening, 42 (17.0%) in diagnostics, 46 (18.6%) in prognostication, and 7 (2.9%) in treatment. 40 (16.3%) used AI as a tool for clinical/epidemiological research and 87 (35.2%) discussed multiple applications of AI. A diverse range of cancers were represented in the studies, including hematologic malignancies. Our scoping review identified three overarching themes in the literature, which largely focused on how AI might improve health equity in oncology. These included: (1) the potential for AI reduce disparities by improving access to health services in resource-limited settings through applications such as low-cost cancer screening technologies and decision support systems; (2) the ability of AI to mitigate bias in clinical decision-making through algorithms that alert clinicians to potential sources of bias thereby allowing for more equitable and individualized care; (3) the use of AI as a research tool to identify disparities in cancer outcomes based on factors such as race, gender and socioeconomic status, and thus inform health policy. While most of the literature emphasized the positive impact of AI in oncology, there was only limited discussion of AI's potential adverse effects on health equity . Despite engaging with the use of AI in resource-limited settings, ethical issues surrounding data extraction and AI trials in low-resource settings were infrequently raised. Similarly, AI's potential to reinforce bias and widen disparities in cancer care was under-examined despite engagement with related topics of bias in clinical decision-making. Conclusion: The overwhelming majority of the literature identified by our scoping review highlights the benefits of AI applications in oncology, including its potential to improve access to care in low-resource settings, mitigate bias in clinical decision-making, and identify disparities in cancer outcomes. However, AI's potential negative impacts on health equity in oncology remain underexplored: ethical issues arising from deploying AI technologies in low-resources settings, and issues of bias in datasets and algorithms were infrequently discussed in articles dealing with related themes. Disclosures No relevant conflicts of interest to declare.

Mobile Health App for Chronic Wound Management (Preprint)

2021 ◽  
Author(s):  
David Reifs ◽  
Ramon Reig Bolaño ◽  
Francesc Garcia Cuyas ◽  
Marta Casals Zorita ◽  
Sergi Grau Carrion
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Clinical Practice ◽  
Chronic Wound ◽  
Mobile App ◽  
Assessment System ◽  
Management Tool ◽  
Cloud Platform ◽  
Wound Assessment ◽  
The Impact

BACKGROUND Chronic ulcers, and especially ulcers affecting the lower extremities and their protracted evolution, are a health problem with significant socio-economic repercussions. The patient's quality of life often deteriorates, leading to serious personal problems for the patient and, in turn, major care challenges for healthcare professionals. Our study proposes a new approach for assisting wound assessment and criticality with an integrated framework based on a Mobile App and a Cloud platform, supporting the practitioner and optimising organisational processes. This framework, called Clinicgram, uses a decision-making support method, such as morphological analysis of wounds and artificial intelligence algorithms for feature classification and a system for matching similar cases via an easily accessible and user-friendly mobile app, and assesses the clinician to choose the best treatment. OBJECTIVE The main objective of this work is to evaluate the impact of the incorporation of Clinicgram, a mobile App and a Cloud platform with Artificial Intelligence algorithms to help the clinician as a decision support system to assess and evaluate correct treatments. Second objective evaluates how the professional can benefit from this technology into the real clinical practice, how it impacts patient care and how the organisation’s resources can be optimised. METHODS Clinicgram application and framework is a non-radiological clinical imaging management tool that is incorporated into clinical practice. The tool will also enable the execution of the different algorithms intended for assessment in this study. With the use of computer vision and supervised learning techniques, different algorithms are implemented to simplify a practitioner's task of assessment and anomaly spotting in clinical cases. Determining the area of interest of the case automatically and using it to assess different wound characteristics such as area calculation and tissue classification, and detecting different signs of infection. An observational and an objective study have been carried out that will allow obtaining clear indicators of the level of usability in clinical practice. RESULTS A total of 2,750 wound pictures were taken by 10 nurses for analysis during the study from January 2018 to November 2021. Objective results have been obtained from the use and management of the application, important feedback from professionals with a score of 5.55 out of 7 according to the mHealth App Usability Questionnaire. It has also been possible to collect the most present type of wound according to Resvech 2.0 of between 6 and 16 points of severity, and highlight the collection of images of between 0 and 16 cm2 of area 88%, with involvement of subcutaneous tissue 53.21%, with the presence of granulated tissue 59.16% and necrotic 30.29% and with a wet wound bed 61.54%. The usage of app to upload samples increase from 31 to 110 samples per month from 2018 to 2021. CONCLUSIONS Our real-world assessment demonstrates the effectiveness and reliability of the wound assessment system, increasing professional efficiency, reducing data collection time during the visit and optimising costs-effectivity in the healthcare organisation by reducing treatment variability. Also, the comfort of the professional and patient. Incorporating a tool such as Clinicgram into the chronic wound assessment and monitoring process adds value, reduction of errors and improves both the clinical practice process time, while also improving decision-making by the professional and consequently having a positive impact on the patient's wound healing process.

Artificial Intelligence Applications in the Auditing Profession: A Literature Review

2021 ◽  
Author(s):  
Ghayah Almufadda ◽  
Nora Almezeini
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Hiring Practices ◽  
Accounting Firms ◽  
Effective Implementation ◽  
Initial Decision ◽  
Essential Questions ◽  
Selective Bibliography ◽  
The Impact ◽  
Big Four

This paper investigates some essential questions that might interest auditors regarding the impact of artificial intelligence (AI) applications on the auditing profession by reviewing a selective bibliography of papers published mainly between 2016 and 2020. It discusses the major AI applications in the auditing field and explores the associated benefits in increasing auditing work’s effectiveness, efficiency, and quality. It further illustrates the major internal critical considerations that should be taken into account before AI application adoption in auditing practices, from initial decision-making to the use of proper countermeasures, to ensure the successful and effective implementation of AI applications. The extent to which AI applications in the accounting and auditing field might affect current hiring practices and threaten an auditor’s job, as performed today, is discussed and various debates and contradictory opinions are presented. The major AI applications adopted by the Big Four accounting firms are also discussed.

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