scholarly journals Ethical issues on artificial intelligence in radiology: how is it reported in research articles? The current state and future directions

2021 ◽  
Vol 90 (2) ◽  
pp. e513
Author(s):  
Tomasz Piotrowski ◽  
Joanna Kazmierska ◽  
Mirosława Mocydlarz-Adamcewicz ◽  
Adam Ryczkowski
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Real Life ◽  
Exclusion Criteria ◽  
Future Directions ◽  
Current State ◽  
Ethical Status ◽  
The Status ◽  
Clinical Questions ◽  
Almost All

Background. This paper evaluates the status of reporting information related to the usage and ethical issues of artificial intelligence (AI) procedures in clinical trial (CT) papers focussed on radiology issues as well as other (non-trial) original radiology articles (OA). Material and Methods. The evaluation was performed by three independent observers who were, respectively physicist, physician and computer scientist. The analysis was performed for two groups of publications, i.e., for CT and OA. Each group included 30 papers published from 2018 to 2020, published before guidelines proposed by Liu et al. (Nat Med. 2020; 26:1364-1374). The set of items used to catalogue and to verify the ethical status of the AI reporting was developed using the above-mentioned guidelines. Results. Most of the reviewed studies, clearly stated their use of AI methods and more importantly, almost all tried to address relevant clinical questions. Although in most of the studies, patient inclusion and exclusion criteria were presented, the widespread lack of rigorous descriptions of the study design apart from a detailed explanation of the AI approach itself is noticeable. Few of the chosen studies provided information about anonymization of data and the process of secure data sharing. Only a few studies explore the patterns of incorrect predictions by the proposed AI tools and their possible reasons. Conclusion. Results of review support idea of implementation of uniform guidelines for designing and reporting studies with use of AI tools. Such guidelines help to design robust, transparent and reproducible tools for use in real life.

scholarly journals Ethics as a Service: A Pragmatic Operationalisation of AI Ethics

2021 ◽  
Author(s):  
Jessica Morley ◽  
Anat Elhalal ◽  
Francesca Garcia ◽  
Libby Kinsey ◽  
Jakob Mökander ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Ethics Codes ◽  
Practical Design ◽  
Ethical Design ◽  
Ai Ethics ◽  
Algorithmic Design ◽  
Almost All ◽  
Technical Guidance ◽  
Tools And Methods

AbstractAs the range of potential uses for Artificial Intelligence (AI), in particular machine learning (ML), has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory of AI ethics principles and the practical design of AI systems. In previous work, we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible (and thus vulnerable to ethics washing) or too strict (unresponsive to context). This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service.’

A Survey on Document-level Neural Machine Translation

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Sameen Maruf ◽  
Fahimeh Saleh ◽  
Gholamreza Haffari
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Research Field ◽  
Translation Process ◽  
Future Directions ◽  
Translation Quality ◽  
Current State ◽  
Evaluation Strategies ◽  
Almost All ◽  
Document Level

Machine translation (MT) is an important task in natural language processing (NLP), as it automates the translation process and reduces the reliance on human translators. With the resurgence of neural networks, the translation quality surpasses that of the translations obtained using statistical techniques for most language-pairs. Up until a few years ago, almost all of the neural translation models translated sentences independently , without incorporating the wider document-context and inter-dependencies among the sentences. The aim of this survey article is to highlight the major works that have been undertaken in the space of document-level machine translation after the neural revolution, so researchers can recognize the current state and future directions of this field. We provide an organization of the literature based on novelties in modelling and architectures as well as training and decoding strategies. In addition, we cover evaluation strategies that have been introduced to account for the improvements in document MT, including automatic metrics and discourse-targeted test sets. We conclude by presenting possible avenues for future exploration in this research field.

Electronic Procurement in the Construction Industry

2012 ◽  
pp. 118-161 ◽  
Author(s):  
Robert Eadie ◽  
Srinath Perera ◽  
George Heaney
Keyword(s):  
Construction Industry ◽  
The State ◽  
Future Directions ◽  
Reference Guide ◽  
Electronic Procurement ◽  
Current State ◽  
Informed Decisions ◽  
The Status ◽  
Insight Into

The benefits of e-business have been widely promoted but the Architecture, Engineering, and Construction (AEC) sector has lagged behind other sectors in the adoption of e-procurement. The prospective benefits for the AEC sector are suggested by the proven advantages of general e-procurement where adoption has been faster and deeper. However, several studies indicated that barely 20% of documentation is tendered electronically, suggesting there are barriers to e-procurement. In order to promote adoption of e-procurement in the AEC sector, it is important to establish the status of the industry and identify the drivers as well as barriers to e-procurement. This chapter provides a detailed discussion of the state of the industry and its drivers and barriers while ranking these according to its importance. It acts as a reference guide to allow those implementing e-procurement in construction to make informed decisions as to where to focus their efforts to achieve successful realisation incorporating the benefits and avoiding the pitfalls in the process. The chapter also provides some insight into the current state, trends, and future directions of e-procurement in the construction industry.

scholarly journals Use of Artificial Intelligence in Nononcologic Interventional Radiology: Current State and Future Directions

2021 ◽  
Author(s):  
Rohil Malpani ◽  
Christopher W. Petty ◽  
Neha Bhatt ◽  
Lawrence H. Staib ◽  
Julius Chapiro
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Language Processing ◽  
Vascular Imaging ◽  
Future Directions ◽  
Current State ◽  
The Future ◽  
Procedural Planning ◽  
Applications Of Ai

AbstractThe future of radiology is disproportionately linked to the applications of artificial intelligence (AI). Recent exponential advancements in AI are already beginning to augment the clinical practice of radiology. Driven by a paucity of review articles in the area, this article aims to discuss applications of AI in nononcologic IR across procedural planning, execution, and follow-up along with a discussion on the future directions of the field. Applications in vascular imaging, radiomics, touchless software interactions, robotics, natural language processing, postprocedural outcome prediction, device navigation, and image acquisition are included. Familiarity with AI study analysis will help open the current “black box” of AI research and help bridge the gap between the research laboratory and clinical practice.

Report from the 2nd Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '20)

2021 ◽  
Vol 46 (2) ◽  
pp. 28-29
Author(s):  
Benoît Vanderose ◽  
Julie Henry ◽  
Benoît Frénay ◽  
Xavier Devroey
Keyword(s):  
Artificial Intelligence ◽  
Software Engineering ◽  
Engineering Education ◽  
State Of The Art ◽  
Future Directions ◽  
The Past ◽  
Software Engineering Education ◽  
Current State ◽  
Amount Of Knowledge ◽  
Software Engineering Tools

In the past years, with the development and widespread of digi- tal technologies, everyday life has been profoundly transformed. The general public, as well as specialized audiences, have to face an ever-increasing amount of knowledge and learn new abilities. The EASEAI workshop series addresses that challenge by look- ing at software engineering, education, and arti cial intelligence research elds to explore how they can be combined. Speci cally, this workshop brings together researchers, teachers, and practi- tioners who use advanced software engineering tools and arti cial intelligence techniques in the education eld and through a trans- generational and transdisciplinary range of students to discuss the current state of the art and practices, and establish new future directions. More information at https://easeai.github.io.

scholarly journals THE NEED OF EDUCATION IN THE FIELD OF DOMESTIC VIOLENCE

2019 ◽  
Vol 3 ◽  
pp. 327
Author(s):  
Peter Papáček
Keyword(s):  
Domestic Violence ◽  
Research Result ◽  
Real Life ◽  
Classical Type ◽  
Legal Aid ◽  
Educational Model ◽  
Main Research ◽  
Current State ◽  
The Status ◽  
Legal Consequences

The paper deals with the thesis that our classical type of education is not always enough to prepare us for the real life matters. One of those matters, a really negative one, is the domestic violence. It is a not very comfortable issue to talk about or to admit its existence in our lives. Furthermore, it deals with the possibility to find a place for this education issue in our educational system and its curriculums. The main aim is to prepare and to give the knowledge about the matter of domestic violence to the youngsters at a reasonable age. It could help in the field of prevention and even in the situations when we have to face the domestic violence – to identify its forms, what can the legal consequences be, how can we act, how can the legal authorities help, what are their competences and how or where can we get legal aid. The paper uses mainly analytical methods to examine the current state and comparative methods too, to compare the current state and the status recommended by the aim of the paper. The main research result is an educational model in the field of domestic violence which is practical and useful in real life situations. 

scholarly journals Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

2021 ◽  
Vol 18 (3) ◽  
pp. 1117
Author(s):  
Tarik Alafif ◽  
Abdul Muneeim Tehame ◽  
Saleh Bajaba ◽  
Ahmed Barnawi ◽  
Saad Zia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Diagnosis And Treatment ◽  
Future Directions ◽  
Health Community ◽  
The World ◽  
The Status ◽  
And Performance

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

Artificial Intelligence and Innovation in Brazilian Justice

2021 ◽  
pp. 1-10
Author(s):  
Fausto Martin De Sanctis
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Ethical Issues ◽  
Learning System ◽  
Bad Faith ◽  
Legal Cases ◽  
Judicial Decisions ◽  
Current State ◽  
Human Thinking ◽  
The Impact

Abstract Artificial intelligence can bring benefits to legal practice, providing agility and precision. It can allow judicial decisions to be the result of the combination of algorithms, enabling the development of a system based on machine learning. This article seeks to demonstrate the current state of the use of artificial intelligence in the Brazilian justice system with the impact of the development of a deep learning system, merely the result of the automation of textual analyses of legal cases, which now serve as models. Reflection is more than necessary given the ethical issues that can arise in view of the inherent precepts that are usually impregnated in the judicial function. Civil servants, lawyers, prosecutors and judges should be guided by a pertinent regulation of new technologies and reflect on whether judicial decisions would be the result of human thinking or not, in addition to the risk that they can carry when the models are biased, in good or bad faith, due to erroneous classification or misinformation in the system.

scholarly journals Comprehensive Literature Review of Hyperpolarized Carbon-13 MRI: The Road to Clinical Application

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 219
Author(s):  
Michael Vaeggemose ◽  
Rolf F. Schulte ◽  
Christoffer Laustsen
Keyword(s):  
Literature Review ◽  
Clinical Application ◽  
Clinical Applications ◽  
Metabolic Imaging ◽  
Limiting Factor ◽  
Future Directions ◽  
The Road ◽  
Current State ◽  
Comprehensive Literature Review ◽  
The Status

This review provides a comprehensive assessment of the development of hyperpolarized (HP) carbon-13 metabolic MRI from the early days to the present with a focus on clinical applications. The status and upcoming challenges of translating HP carbon-13 into clinical application are reviewed, along with the complexity, technical advancements, and future directions. The road to clinical application is discussed regarding clinical needs and technological advancements, highlighting the most recent successes of metabolic imaging with hyperpolarized carbon-13 MRI. Given the current state of hyperpolarized carbon-13 MRI, the conclusion of this review is that the workflow for hyperpolarized carbon-13 MRI is the limiting factor.

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