AI ethics in April 2021

This study aimed to discuss the research efforts in developing virtual reality (VR) technology for different training applications. To begin with, we describe how VR training experiences are typically created and delivered using the current software and hardware. We then discuss the challenges and solutions of applying VR training to different application domains, such as first responder training, medical training, military training, workforce training, and education. Furthermore, we discuss the

The purpose of this paper is to analyse customer experience (CX) in the fintech sector. Fintech is a dynamic and innovative field that fully benefits from advances in information and communication technology. The concept of customer experience is multidimensional, analysed from various perspectives, and with distinct valences in different industries. Based on the stimulus-organism-response (S-O-R) approach, we proposed a model in which customer experience in fintech is the result of customer’s e

BACKGROUND: The triglyceride-glucose index (TyG index) has been proposed as a simple and reliable alternative insulin resistance (IR) marker, while the homeostasis model assessment for IR (HOMA-IR) is the most frequently used index. Few studies have evaluated the role of IR assessed by the TyG index and HOMA-IR on arterial stiffness in a type 2 diabetes (T2D) population with a high risk of increased arterial stiffness. We aimed to investigate the association of the TyG index and HOMA-IR with art

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of G

In Low- and Middle- Income Countries (LMICs), machine learning (ML) and artificial intelligence (AI) offer attractive solutions to address the shortage of health care resources and improve the capacity of the local health care infrastructure. However, AI and ML should also be used cautiously, due to potential issues of fairness and algorithmic bias that may arise if not applied properly. Furthermore, populations in LMICs can be particularly vulnerable to bias and fairness in AI algorithms, due t

Nature-based solutions (NBS) for hydro-meteorological risks (HMRs) reduction and management are becoming increasingly popular, but challenges such as the lack of well-recognised standard methodologies to evaluate their performance and upscale their implementation remain. We systematically evaluate the current state-of-the art on the models and tools that are utilised for the optimum allocation, design and efficiency evaluation of NBS for five HMRs (flooding, droughts, heatwaves, landslides, and

BACKGROUND: The SARS-CoV-2 variant B.1.1.7 was first identified in December, 2020, in England. We aimed to investigate whether increases in the proportion of infections with this variant are associated with differences in symptoms or disease course, reinfection rates, or transmissibility. METHODS: , for the two incidence estimates. FINDINGS: fell below 1 during regional and national lockdowns, even in regions with high proportions of infections with the B.1.1.7 variant. INTERPRETATION: The lack

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analy

Abstract Digital assistant is a recent advancement benefited through data-driven innovation. Though digital assistants have become an integral member of user conversations, but there is no theory that relates user perception towards this AI powered technology. The purpose of the research is to investigate the role of technology attitude and AI attributes in enhancing purchase intention through digital assistants. A conceptual model is proposed after identifying three major AI factors namely, per

BACKGROUND: Social media has been widely used for health-related purposes, especially during the COVID-19 pandemic. Previous reviews have summarized social media uses for a specific health purpose such as health interventions, health campaigns, medical education, and disease outbreak surveillance. The most recent comprehensive review of social media uses for health purposes, however, was conducted in 2013. A systematic review that covers various health purposes is needed to reveal the new usages

BACKGROUND/INTRODUCTION: Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. METHODS: The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors

Abstract While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically , to understa

Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis