scholarly journals The impact of the introduction of artificial intelligence in radiology and its potential legal implications in the UK and Ireland

BJR|Open ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 20200030
Author(s):  
Toni Anderson ◽  
William C Torreggiani ◽  
Peter L Munk ◽  
Paul I Mallinson
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Human Behaviour ◽  
Diagnostic Radiology ◽  
Legal Implications ◽  
Computer Based ◽  
The Uk ◽  
The Impact ◽  
Missed Diagnoses

Artificial intelligence (AI) has been defined as a branch of computer science dealing with the capability and simulation of a machine to imitate intelligent human behaviour. Diagnostic radiology, being a computer-based service, is unsurprisingly at the forefront of the discussion of the use of AI in medicine. There are however differing schools of thought regarding its use; namely, will AI eventually replace the radiologist? Or indeed will it ever be fully capable of replacing radiology as a speciality, but rather be used as an aid to the profession whereby a human’s input will always be required? Furthermore, what will the legal implications of AI in radiology mean to the profession? Who will be liable for missed diagnoses? Is it possible that the introduction of AI to radiology will in fact make the profession busier?

Leveraging Twitter Using Artificial Intelligence to Explore Mental Health Insights in the UK During the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Christopher Marshall ◽  
Kate Lanyi ◽  
Rhiannon Green ◽  
Georgie Wilkins ◽  
Fiona Pearson ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Language Processing ◽  
Mental Health Problems ◽  
Well Being ◽  
Research Activity ◽  
Health Crisis ◽  
Key Topics ◽  
The Uk ◽  
The Impact

BACKGROUND There is increasing need to explore the value of soft-intelligence, leveraged using the latest artificial intelligence (AI) and natural language processing (NLP) techniques, as a source of analysed evidence to support public health research activity and decision-making. OBJECTIVE The aim of this study was to further explore the value of soft-intelligence analysed using AI through a case study, which examined a large collection of UK tweets relating to mental health during the COVID-19 pandemic. METHODS A search strategy comprising a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a specialist NLP platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. Qualitative document analysis was carried out to further explore and expand upon the results generated by the NLP platform. All collated tweets were anonymised RESULTS We identified and analysed 286,902 tweets posted from UK user accounts from 23 July 2020 to 6 January 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume and sentiment appeared to coincide with key changes to any local and/or national social-distancing measures. Tweets around mental health were polarising, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. CONCLUSIONS Through the primary use of an AI-based NLP platform, we were able to rapidly mine and analyse emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analysed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

Artificial Intelligence and Machine Learning in Radiology

2019 ◽  
pp. 199-226
Author(s):  
Michael A. Bruno
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Diagnostic Radiology ◽  
Job Description ◽  
Error Prevention ◽  
Human Endeavor ◽  
Key Concepts ◽  
The Impact

This final chapter, which assumes no prior reader knowledge of the topic, reviews the promise of artificial intelligence (AI), especially machine learning and deep learning in radiology. We initially discuss key concepts in the field of AI and gain a broad overview of the field and its potential, as well as the impact it is having on multiple areas of human endeavor. Subsequently, we focus on current and projected aspects of AI as applied to diagnostic radiology, specifically on how AI might provide an avenue for error prevention and remediation in radiology. The possible impact of AI in changing the radiologist’s role and basic job description is also considered.

scholarly journals Deep Learning does not Replace Bayesian Modeling : Comparing research use via citation counting

2021 ◽  
Author(s):  
Breck Baldwin
Keyword(s):  
Artificial Intelligence ◽  
Popular Culture ◽  
Deep Learning ◽  
Computer Science ◽  
Bayesian Modeling ◽  
Citation Count ◽  
Funding Agency ◽  
Research Use ◽  
Learning Approaches ◽  
The Impact

One could be excused for assuming that deep learning had or will soon usurp all credible work in reasoning, artificial intelligence and statistics, but like most ‘meme’ class broad generalizations the concept does not hold up to scrutiny. Memes don’t generally matter since the experts will always know better but in the case of Bayesian software like Stan and PyMC3 even its developers and advocates bemoan the apparent dominance of deep learning as manifested in popular culture, breathtaking performance and most problematically from funding agency peer review that impacts our ability to further advance the field. The facts however do not support the assumed dominance of deep learning in science upon closer examination. This letter simply makes the argument by the crudest of possible metrics, citation count, that once Computer Science is subtracted, Bayesian software accounts for nearly a third of research citations. Stan and PyMC3 dominate some fields, PyTorch, Keras and TensorFlow dominate others with lots of variation in between. Bayesian and deep learning approaches are related but very different technologies in goals, implementation and applicability with little actual overlap so this is not a surprise. While deep learning is backed by industry behemoths (Google, Facebook) the Bayesian efforts are not and it would behoove funders to recognize the impact of Bayesian software given its centrality to science.

scholarly journals The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

2021 ◽  
Vol 8 ◽  
Author(s):  
Raffaele Nuzzi ◽  
Giacomo Boscia ◽  
Paola Marolo ◽  
Federico Ricardi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Computer Science ◽  
New Technology ◽  
Eye Diseases ◽  
Human Intelligence ◽  
Basic Principles ◽  
The Impact ◽  
And Training ◽  
Clinical Overview

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology.

The impact of fire and rescue service first responders on participant behaviour during chemical, biological, radiological and nuclear (CBRN)/Hazmat incidents

2020 ◽  
Vol 9 (3) ◽  
pp. 283-298
Author(s):  
Francis Long ◽  
Georgia Bateman ◽  
Arnab Majumdar
Keyword(s):  
Relevant Literature ◽  
First Responders ◽  
Human Behaviour ◽  
Effective Response ◽  
Content Type ◽  
Rescue Service ◽  
Response Planning ◽  
The Uk ◽  
Behavioural Aspects ◽  
The Impact

PurposeDecontamination following chemical, biological, radiological and nuclear (CBRN)/Hazmat incidents is a critical activity carried out in order to mitigate and contain the risk posed by any hazardous materials involved. Human behaviour plays a crucial role in such incidents, as casualties will have little understanding of the situation they find themselves in, leading to uncertainty in what actions to take. This will result in very difficult circumstances within which first responders must operate. However, the importance of human behaviour appears to be a fundamental element being missed in the preparation, training and planning assumptions being made by emergency services and planners in preparation for these events.Design/methodology/approachThis paper looks to understand the scope of this omission by reviewing relevant literature on the subject and engaging with Fire and Rescue Service personnel and managers in the UK. This study utilised semi-structured interviews with 10 Fire and Rescue Service Mass Decontamination Operatives, four Fire and Rescue Service Hazardous Material Advisers and three Fire and Rescue Service Strategic Officers participating. These interviews were then analysed using a thematic framework to identified key themes from the research which were then validated using two independent researchers to provide an inter-rater reliability measure. Finally, a follow-up validation questionnaire was also developed to test the validity of the themes identified and this was completed by another with 36 Fire and Rescue Service Mass Decontamination Operatives.FindingsBoth the literature review and interviews undertaken with emergency responders and mangers demonstrated the crucial importance of accounting for behavioural aspects in these situations especially in regards to the likely levels of compliance to be expected by responders and the potential problem of casualties not remaining at the scene of an incident to undergo decontamination.Originality/valueThis research identifies a number of key themes so far not recognized through any other research and in doing so offers insights into potential flaws in the UK Fire and Rescue Service response planning for CBRN/Hazmat incidents requiring mass decontamination. It is intended that this research will inform further study into the areas identified in order to ensure gaps in planning, training and strategies for mass decontamination operations can be more fully informed and if required allow for a more effective response.

The Impact of Computer-Based Alternatives on the Use of Animals in Undergraduate Teaching: A Pilot Study

1995 ◽  
Vol 23 (4) ◽  
pp. 521-530
Author(s):  
David Dewhurst ◽  
Linda Jenkinson
Keyword(s):  
Pilot Study ◽  
Academic Staff ◽  
University Teaching ◽  
Undergraduate Teaching ◽  
Computer Assisted ◽  
Staff Perceptions ◽  
Alternative Approach ◽  
Computer Based ◽  
The Uk ◽  
The Impact

— The impact of computer-assisted learning (CAL) packages on the use of animals in university teaching has been investigated in universities in the UK and abroad. The pilot study has focused on two issues: a) academic staff perceptions of the usability of CAL packages designed to offer an alternative to animal practicals in physiology and pharmacology; and b) whether the use of such programs has led to a reduction in the number of animals used. A questionnaire survey of purchasers of a minimum of three commercially available programs, which offer an alternative approach to traditional laboratory experiments, was conducted. The study found that in most departments the packages were used in a staff-supervised learning situation, to either replace or support a practical class. Their use saved academic and non-academic staff time, and they were considered to be less expensive and an effective and enjoyable mode of student learning. It was also clear that their use had contributed to a significant reduction in animal use.

scholarly journals The impact of computer based training on technical training in industry*

1988 ◽  
Vol 4 (2) ◽  
Author(s):  
David M. Goldsmith
Keyword(s):  
Artificial Intelligence ◽  
Instructional Design ◽  
Technical Training ◽  
Computer Based Training ◽  
Computer Based ◽  
The Impact

<span>There is growing acceptance in industry of Computer Based Training (CBT) as an appropriate medium for technical training. Few CBT systems are designed to meet the flexible instructional design needs or simulation requirements of such training, or are capable in the longer term of adaption to the use of Artificial Intelligence (AI) principles, Serious trainers and educators have had to re-examine system specifications and capabilities before embarking further on the path to automated training.</span>

scholarly journals Tax Implications of Brexit – The Road Ahead

2017 ◽  
pp. 39-48
Author(s):  
Dr. Kanwal D. P. Singh
Keyword(s):  
European Union ◽  
The European Union ◽  
Corporate Tax ◽  
Economic Changes ◽  
The Road ◽  
Legal Standing ◽  
The People ◽  
Legal Implications ◽  
The Uk ◽  
The Impact

On 23rd June 2016, the people of United Kingdom voted to exit from the European Union. This exit impacts the economies of the UK and EU both in many ways. Numerous changes in taxation structure, tariffs, business methodology will be seen. These tax implications and the impact of the exit decision on economy and business are unclear. This article analyses the legal implications and the process of exit to be followed after the referendum and the various strategies to proceed and their legal standing are discussed. The main issues that the economies of UK and EU shall face are discussed in the article. It also discusses the potential economic changes that might occur along with the impact of Brexit on corporate tax structure, social security, trade and other areas respectively.

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