AI ethics in September 2022

Abstract Background Artificial intelligence (AI) technology has recently seen rapid advancement, with an expanding role and scope in nursing education and healthcare. This study identifies the influence of AI ethics awareness, attitude toward AI, anxiety, and self-efficacy on nursing students’ behavioral intentions to use AI-based healthcare technology. Methods The participants included 189 nursing students in Gyeonggi-do, with data collected from November to December 2021 using self-reported qu

BACKGROUND: There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typically involving expensive and sometimes invasive tests not commonly available outside highly specialised clinical settings. We aimed to develop a deep learning algorithm that could use retinal photographs alone, which is the most common method of non-invasive imaging the retina to detect Alzheimer's disease-dementia. METHODS: In this retrospective, mul

This article analyses and reflects on the ethical aspects of using artificial intelligence (AI) systems in educational contexts. On the one hand, the impact of AI in the field of education is addressed from the perspective of the Sustainable Development Goals (specifically, SDG4) of the UNESCO 2030 Agenda, describing the opportunities for its use by teachers and students. On the other hand, there is an emphasis on the uncertainties caused by the fears of some who think, for example, that AI robo

Artificial Intelligence (AI) is viewed as having potential for significant economic and social impact. However, its history of boom and bust cycles can make potential adopters wary. A cross-sectional, qualitative study was carried out, with a purposive sample of AI experts from research, development and business functions, to gain a deeper understanding of the adoption process. Technology Readiness Levels were used as a benchmark against which the experts could align their experiences. A model o

Abstract Education is a unique area for application of artificial intelligence (AI). In this article, the augmentation perspective and the concept of hybrid intelligence are introduced to frame our thinking about AI in education. The involvement of quadruple helix stakeholders (i.e., researchers, education professionals, entrepreneurs, and policymakers) is necessary to understand the opportunities and challenges of different educational use cases from an integrated point of view. To facilitate a

Purpose Metaverse blends the physical and virtual worlds, revolutionizing how hospitality customers and hospitality organizations facilitate the co-creation of transformational experiences and values. This paper aims to explore the opportunities and challenges that Metaverse introduces for hospitality and tourism management and marketing as part of the customer experience and value co-creation process. This paper also discusses how the advent and development of Metaverse can potentially transfor

The present study uses a bibliometric and systematic literature review (SLR) to examine the use of Building Information Modeling (BIM), the Internet of Things (IoT), and Digital Twins (DT) in the construction industry. The network visualization and other approaches based on the Web of Science (WOS) database and the patterns of research interactions were explored in 1879 academic publications using co-occurrence and co-citation investigations. Significant publications, conferences, influential au

Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs. Our approac

In tasks involving the interpretation of medical images, suitably trained machine-learning models often exceed the performance of medical experts. Yet such a high-level of performance typically requires that the models be trained with relevant datasets that have been painstakingly annotated by experts. Here we show that a self-supervised model trained on chest X-ray images that lack explicit annotations performs pathology-classification tasks with accuracies comparable to those of radiologists.

Deep Residual Networks have recently been shown to significantly improve the performance of neural networks trained on ImageNet, with results beating all previous methods on this dataset by large margins in the image classification task. However, the meaning of these impressive numbers and their implications for future research are not fully understood yet. In this survey, we will try to explain what Deep Residual Networks are, how they achieve their excellent results, and why their successful i

Sustainability orientation has a positive effect on startups' initial valuation and a negative effect on their post-funding financial performance. All else equal, improving sustainability orientation by one standard deviation increases startups' funding amount by 28 % and decreases investors' abnormal returns per post-funding year by 16 %. The results hold in a large sample of blockchain-based crowdfunding campaigns, also known as Initial Coin Offerings (ICOs) or token offerings. A key contribut

PURPOSE: The expanded use of clinical tools that incorporate artificial intelligence (AI) methods has generated calls for specific competencies for effective and ethical use. This qualitative study used expert interviews to define AI-related clinical competencies for health care professionals. METHOD: In 2021, a multidisciplinary team interviewed 15 experts in the use of AI-based tools in health care settings about the clinical competencies health care professionals need to work effectively with

An influential line of thinking in behavioral science, to which the two authors have long subscribed, is that many of society's most pressing problems can be addressed cheaply and effectively at the level of the individual, without modifying the system in which the individual operates. We now believe this was a mistake, along with, we suspect, many colleagues in both the academic and policy communities. Results from such interventions have been disappointingly modest. But more importantly, they

The increasing availability of biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing have set the stage for the development of multimodal artificial intelligence solutions that capture the complexity of human health and disease. In this Review, we outline the key applications enabled, along with the technical and analytical challenges. We explore opportunities in personalized medici