The Artificial Intelligence in Digital Radiology: Part 2: Towards an Investigation of acceptance and consensus on the Insiders

Background. The study deals with the introduction of the artificial intelligence in digital radiology. There is a growing interest in this area of scientific research in acceptance and consensus studies involving both insiders and the public, based on surveys focused mainly on single professionals. Purpose. The goal of the study is to perform a contemporary investigation on the acceptance and the consensus of the three key professional figures approaching in this field of application: (1) Medical specialists in image diagnostics: the medical specialists (MS)s; (2) experts in physical imaging processes: the medical physicists (MP)s; (3) AI designers: specialists of applied sciences (SAS)s. Methods. Participants (MSs = 92: 48 males/44 females, averaged age 37.9; MPs = 91: 43 males/48 females, averaged age 36.1; SAS = 90: 47 males/43 females, averaged age 37.3) were properly recruited based on specific training. An electronic survey was designed and submitted to the participants with a wide range questions starting from the training and background up to the different applications of the AI and the environment of application. Results. The results show that generally, the three professionals show (a) a high degree of encouraging agreement on the introduction of AI both in imaging and in non-imaging applications using both standalone applications and/or mHealth/eHealth, and (b) a different consent on AI use depending on the training background. Conclusions. The study highlights the usefulness of focusing on both the three key professionals and the usefulness of the investigation schemes facing a wide range of issues. The study also suggests the importance of different methods of administration to improve the adhesion and the need to continue these investigations both with federated and specific initiatives.

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Lessons from the COVID-19 Pandemic on the Use of Artificial Intelligence in Digital Radiology: The Submission of a Survey to Investigate the Opinion of Insiders

Healthcare ◽

10.3390/healthcare9030331 ◽

2021 ◽

Vol 9 (3) ◽

pp. 331

Author(s):

Daniele Giansanti ◽

Ivano Rossi ◽

Lisa Monoscalco

Keyword(s):

Artificial Intelligence ◽

General Purpose ◽

Point Of View ◽

General Point ◽

Thoracic Radiography ◽

Digital Radiology ◽

Electronic Survey ◽

Radiology Technicians ◽

Medical Radiology ◽

Great Development

The development of artificial intelligence (AI) during the COVID-19 pandemic is there for all to see, and has undoubtedly mainly concerned the activities of digital radiology. Nevertheless, the strong perception in the research and clinical application environment is that AI in radiology is like a hammer in search of a nail. Notable developments and opportunities do not seem to be combined, now, in the time of the COVID-19 pandemic, with a stable, effective, and concrete use in clinical routine; the use of AI often seems limited to use in research applications. This study considers the future perceived integration of AI with digital radiology after the COVID-19 pandemic and proposes a methodology that, by means of a wide interaction of the involved actors, allows a positioning exercise for acceptance evaluation using a general purpose electronic survey. The methodology was tested on a first category of professionals, the medical radiology technicians (MRT), and allowed to (i) collect their impressions on the issue in a structured way, and (ii) collect their suggestions and their comments in order to create a specific tool for this professional figure to be used in scientific societies. This study is useful for the stakeholders in the field, and yielded several noteworthy observations, among them (iii) the perception of great development in thoracic radiography and CT, but a loss of opportunity in integration with non-radiological technologies; (iv) the belief that it is appropriate to invest in training and infrastructure dedicated to AI; and (v) the widespread idea that AI can become a strong complementary tool to human activity. From a general point of view, the study is a clear invitation to face the last yard of AI in digital radiology, a last yard that depends a lot on the opinion and the ability to accept these technologies by the operators of digital radiology.

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Corporate Governance of Artificial Intelligence in the Public Interest

Information ◽

10.3390/info12070275 ◽

2021 ◽

Vol 12 (7) ◽

pp. 275

Author(s):

Peter Cihon ◽

Jonas Schuett ◽

Seth D. Baum

Keyword(s):

Artificial Intelligence ◽

Corporate Governance ◽

Law Enforcement ◽

Nonprofit Organizations ◽

Public Interest ◽

Department Of Defense ◽

The Public ◽

Wide Range ◽

The Media ◽

The U.S

Corporations play a major role in artificial intelligence (AI) research, development, and deployment, with profound consequences for society. This paper surveys opportunities to improve how corporations govern their AI activities so as to better advance the public interest. The paper focuses on the roles of and opportunities for a wide range of actors inside the corporation—managers, workers, and investors—and outside the corporation—corporate partners and competitors, industry consortia, nonprofit organizations, the public, the media, and governments. Whereas prior work on multistakeholder AI governance has proposed dedicated institutions to bring together diverse actors and stakeholders, this paper explores the opportunities they have even in the absence of dedicated multistakeholder institutions. The paper illustrates these opportunities with many cases, including the participation of Google in the U.S. Department of Defense Project Maven; the publication of potentially harmful AI research by OpenAI, with input from the Partnership on AI; and the sale of facial recognition technology to law enforcement by corporations including Amazon, IBM, and Microsoft. These and other cases demonstrate the wide range of mechanisms to advance AI corporate governance in the public interest, especially when diverse actors work together.

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Analysis of Public Opinion in Colleges and Universities Based on Wireless Web Crawler Technology in the Context of Artificial Intelligence

Mobile Information Systems ◽

10.1155/2022/7745028 ◽

2022 ◽

Vol 2022 ◽

pp. 1-10

Author(s):

WenNing Wu ◽

ZhengHong Deng

Keyword(s):

Artificial Intelligence ◽

Public Opinion ◽

Colleges And Universities ◽

Web Crawler ◽

The Public ◽

User Influence ◽

Proposed Model ◽

Wide Range ◽

Different Dimensions ◽

The Web

Wi-Fi-enabled information terminals have become enormously faster and more powerful because of this technology’s rapid advancement. As a result of this, the field of artificial intelligence (AI) was born. Artificial intelligence (AI) has been used in a wide range of societal contexts. It has had a significant impact on the realm of education. Using big data to support multistage views of every subject of opinion helps to recognize the unique characteristics of each aspect and improves social network governance’s suitability. As public opinion in colleges and universities becomes an increasingly important vehicle for expressing public opinion, this paper aims to explore the concepts of public opinion based on the web crawler and CNN (Convolutional Neural Network) model. Web crawler methodology is utilised to gather the data given by students of college and universities and mention them in different dimensions. This CNN has robust data analysis capability; this proposed model uses the CNN to analyse the public opinion. Preprocessing of data is done using the oversampling method to maximize the effect of classification. Through the association of descriptions, comprehensive utilization of image information like user influence, stances of comments, topics, time of comments, etc., to suggest guidance phenomenon for various schemes, helps to enhance the effectiveness and targeted social governance of networks. The overall experimentation was carried out in python here in which the suggested methodology was predicting the positive and negative opinion of the students over the web crawler technology with a low rate of error when compared to other existing methodology.

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Elections to EU Parliament in 2019 as Сovered by Russian Media

Вопросы теории и практики журналистики ◽

10.17150/2308-6203.2020.9(4).673-686 ◽

2020 ◽

Vol 9 (4) ◽

pp. 673-686

Author(s):

Natalia Urina ◽

Anastasia Grusha

Keyword(s):

Political Communication ◽

Media Coverage ◽

International Politics ◽

Political Event ◽

The Public ◽

Wide Range ◽

Russian Media ◽

The Media ◽

Number Of Publications ◽

High Degree

In the context of emergence of new political communication channels and new players in the international arena, the problem of perceiving actors of international politics by the public and the content of information about them provided by the media is especially relevant. Not only the exponentially growing number of publications on this topic but also their quality needs particular attention and deep analysis. The EU, whose legislative power is executed by the European Parliament, is one of the key actors of international politics. This work, being part of a full-scale research of Russian media coverage of EU Parliament elections in 2019, aims at studying qualitative and quantitative characteristics of publications on this topic and determining elements of the EU’s media image in Russian media. The quantitative analysis required studying “Medailogia” database and covered both traditional and online national and regional media whose texts mention key words on the elections between May 13 and June 9, 2019 more than twice. The qualitative analysis involved examining the content of publications in the media in the period of April–June 2019, focusing on the topics, volume and completeness of information on the elections, the dynamic of coverage and news hooks in various media, and the genre features of the publications. The results of the study show that EU Parliament elections in 2019 were not a top theme in Russian media. However, the media presented quite a wide range of opinions on the political event and demonstrated a high degree of personification of international politics.

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Artificial Intelligence and the Implementation Challenge (Preprint)

10.2196/preprints.13659 ◽

2019 ◽

Cited By ~ 1

Author(s):

James Shaw ◽

Frank Rudzicz ◽

Trevor Jamieson ◽

Avi Goldfarb

Keyword(s):

Artificial Intelligence ◽

Health Care ◽

Decision Support ◽

Scale Up ◽

Use Cases ◽

The Public ◽

Health Care Work ◽

Wide Range ◽

Science Community ◽

Near Term

BACKGROUND Applications of artificial intelligence (AI) in health care have garnered much attention in recent years, but the implementation issues posed by AI have not been substantially addressed. OBJECTIVE In this paper, we have focused on machine learning (ML) as a form of AI and have provided a framework for thinking about use cases of ML in health care. We have structured our discussion of challenges in the implementation of ML in comparison with other technologies using the framework of Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies (NASSS). METHODS After providing an overview of AI technology, we describe use cases of ML as falling into the categories of decision support and automation. We suggest these use cases apply to clinical, operational, and epidemiological tasks and that the primary function of ML in health care in the near term will be decision support. We then outline unique implementation issues posed by ML initiatives in the categories addressed by the NASSS framework, specifically including meaningful decision support, explainability, privacy, consent, algorithmic bias, security, scalability, the role of corporations, and the changing nature of health care work. RESULTS Ultimately, we suggest that the future of ML in health care remains positive but uncertain, as support from patients, the public, and a wide range of health care stakeholders is necessary to enable its meaningful implementation. CONCLUSIONS If the implementation science community is to facilitate the adoption of ML in ways that stand to generate widespread benefits, the issues raised in this paper will require substantial attention in the coming years.

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Understanding Conformational Entropy in Small Molecules

10.26434/chemrxiv.12671027 ◽

2020 ◽

Author(s):

Lucian Chan ◽

Garrett Morris ◽

Geoffrey Hutchison

Keyword(s):

Small Molecules ◽

Degrees Of Freedom ◽

Absolute Error ◽

Low Energy ◽

Standard Entropy ◽

Free Energies ◽

Conformational Entropy ◽

Wide Range ◽

High Degree ◽

Empirical Corrections

The calculation of the entropy of flexible molecules can be challenging, since the number of possible conformers grows exponentially with molecule size and many low-energy conformers may be thermally accessible. Different methods have been proposed to approximate the contribution of conformational entropy to the molecular standard entropy, including performing thermochemistry calculations with all possible stable conformations, and developing empirical corrections from experimental data. We have performed conformer sampling on over 120,000 small molecules generating some 12 million conformers, to develop models to predict conformational entropy across a wide range of molecules. Using insight into the nature of conformational disorder, our cross-validated physically-motivated statistical model can outperform common machine learning and deep learning methods, with a mean absolute error ≈4.8 J/mol•K, or under 0.4 kcal/mol at 300 K. Beyond predicting molecular entropies and free energies, the model implies a high degree of correlation between torsions in most molecules, often as- sumed to be independent. While individual dihedral rotations may have low energetic barriers, the shape and chemical functionality of most molecules necessarily correlate their torsional degrees of freedom, and hence restrict the number of low-energy conformations immensely. Our simple models capture these correlations, and advance our understanding of small molecule conformational entropy.

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INTERDISCIPLINARY LINKS, INTEGRATIVE APPROACH,HEURISTIC METHODS AND ARTIFICIAL INTELLIGENCE IN THE MANAGEMENT OF INNOVATION - PART 1

Knowledge International Journal ◽

10.35120/kij28051527h ◽

2018 ◽

Vol 28 (5) ◽

pp. 1527-1532

Author(s):

Hristo Patev

Keyword(s):

Artificial Intelligence ◽

Production Systems ◽

Innovation Management ◽

Integrative Approach ◽

Heuristic Methods ◽

Organizational Renewal ◽

Innovation Activities ◽

Management Of Innovation ◽

Inventive Activity ◽

High Degree

In this first work, out of the total of twenty-four, are considered: Integrative approach, interdisciplinary relations and transnational language in the technical and economic fundament of engineering and management, for the purpose of competitive innovation and successful business. Approaches to develop the innovation with a high degree of complexity. Interactive heuristic methods and algorithms for inventive activity, for inspiring and developing new industrial products and services for households and production systems. Implementing an effective business vocabulary for organizational renewal. Introduction of gaming and "art" methods in innovation management. Intensifying innovation activities through an attempt to introduce artificial intelligence into teamwork, with simultaneous implementation of an engineering and non-engineering approach.

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Towards a Customized¬ Awareness Campaign to Educate the Public about COVID-19: Development and Validation of a Messaging Campaign to Promote Precautionary Behavior Using Geospatial Intelligence in Saudi Arabia (Preprint)

10.2196/preprints.26856 ◽

2020 ◽

Author(s):

Mayda Alrige ◽

Hind Bitar Bitar ◽

Maram Meccawi ◽

Balakrishnan Mullachery

Keyword(s):

Artificial Intelligence ◽

Saudi Arabia ◽

Content Validity ◽

Awareness Campaign ◽

Artificial Intelligence Technique ◽

The Public ◽

High Infection ◽

Local Map ◽

The City ◽

Intelligence Technique

BACKGROUND Designing a health promotion campaign is never an easy task, especially during a pandemic of a highly infectious disease, such as Covid-19. In Saudi Arabia, many attempts have been made toward raising the public awareness about Covid-19 infection-level and its precautionary health measures that have to be taken. Although this is useful, most of the health information delivered through the national dashboard and the awareness campaign are very generic and not necessarily make the impact we like to see on individuals’ behavior. OBJECTIVE The objective of this study is to build and validate a customized awareness campaign to promote precautionary health behavior during the COVID-19 pandemic. The customization is realized by utilizing a geospatial artificial intelligence technique called Space-Time Cube (STC) technique. METHODS This research has been conducted in two sequential phases. In the first phase, an initial library of thirty-two messages was developed and validated to promote precautionary messages during the COVID-19 pandemic. This phase was guided by the Fogg Behavior Model (FBM) for behavior change. In phase 2, we applied STC as a Geospatial Artificial Intelligence technique to create a local map for one city representing three different profiles for the city districts. The model was built using COVID-19 clinical data. RESULTS Thirty-two messages were developed based on resources from the World Health Organization and the Ministry of Health in Saudi Arabia. The enumerated content validity of the messages was established through the utilization of Content Validity Index (CVI). Thirty-two messages were found to have acceptable content validity (I-CVI=.87). The geospatial intelligence technique that we used showed three profiles for the districts of Jeddah city: one for high infection, another for moderate infection, and the third for low infection. Combining the results from the first and second phases, a customized awareness campaign was created. This awareness campaign would be used to educate the public regarding the precautionary health behaviors that should be taken, and hence help in reducing the number of positive cases in the city of Jeddah. CONCLUSIONS This research delineates the two main phases to developing a health awareness messaging campaign. The messaging campaign, grounded in FBM, was customized by utilizing Geospatial Artificial Intelligence to create a local map with three district profiles: high-infection, moderate-infection, and low-infection. Locals of each district will be targeted by the campaign based on the level of infection in their district as well as other shared characteristics. Customizing health messages is very prominent in health communication research. This research provides a legitimate approach to customize health messages during the pandemic of COVID-19.

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Unified approach to spatial description of objects terrain departmental registries / cadastres as a perspective basis of the state system territory mapping

Geodesy and Cartography ◽

10.22389/0016-7126-2018-942-12-41-49 ◽

2019 ◽

Vol 942 (12) ◽

pp. 41-49

Author(s):

A.M. Portnov

Keyword(s):

Spatial Data ◽

Spatial Description ◽

Unified Approach ◽

Technological Basis ◽

The Public ◽

Wide Range ◽

The Russian Federation ◽

Map Data ◽

Consistency Verification ◽

Thematic Data

Using unified principles of formation and maintenance of register/cadaster with information about spatial data of landscape objects as the informational and technological basis for updating the public topographic maps and modernization of state cartographic system is proposed. The problems of informational relevancy of unified electronical cartographic basis and capacity of its renovation in case of public cadaster map data. The need to modernize the system of classification and coding of cartographic information, the use of unified standards for the coordinate description of register objects for their topological consistency, verification and updating is emphasized. Implementing such solutions is determined by economical expediency as well as necessity of providing a variety of real thematic data for wide range of consumers in the field of urban planning, territories development and completing the tasks of Governmental program “Digital economy of the Russian Federation”.

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Living machines in our cultural imagination

10.1093/oso/9780199674923.003.0061 ◽

2018 ◽

Author(s):

Michael Szollosy

Keyword(s):

Artificial Intelligence ◽

Science Fiction ◽

Public Perceptions ◽

Human Condition ◽

Cultural Imagination ◽

The Public ◽

The Enlightenment ◽

The Future ◽

Public Imagination ◽

The Human Condition

Public perceptions of robots and artificial intelligence (AI)—both positive and negative—are hopelessly misinformed, based far too much on science fiction rather than science fact. However, these fictions can be instructive, and reveal to us important anxieties that exist in the public imagination, both towards robots and AI and about the human condition more generally. These anxieties are based on little-understood processes (such as anthropomorphization and projection), but cannot be dismissed merely as inaccuracies in need of correction. Our demonization of robots and AI illustrate two-hundred-year-old fears about the consequences of the Enlightenment and industrialization. Idealistic hopes projected onto robots and AI, in contrast, reveal other anxieties, about our mortality—and the transhumanist desire to transcend the limitations of our physical bodies—and about the future of our species. This chapter reviews these issues and considers some of their broader implications for our future lives with living machines.

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