AI ethics in August 2019

As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for thi

Digital technology has changed organizations in an irreversible way. Like the movable type printing accelerated the evolution of our history, digitalization is shaping organizations, work environment and processes, creating new challenges leaders have to face. Social science scholars have been trying to understand this multifaceted phenomenon, however, findings have accumulated in a fragmented and dispersed fashion across different disciplines, and do not seem to converge within a clear picture.

BACKGROUND: Intensive care units (ICUs) face financial, bed management, and staffing constraints. Detailed data covering all aspects of patients' journeys into and through intensive care are now collected and stored in electronic health records: machine learning has been used to analyse such data in order to provide decision support to clinicians. METHODS: Systematic review of the applications of machine learning to routinely collected ICU data. Web of Science and MEDLINE databases were searched

BACKGROUND: Hyperspectral imaging is emerging as a promising approach for plant disease identification. The large and possibly redundant information contained in hyperspectral data cubes makes deep learning based identification of plant diseases a natural fit. Here, we deploy a novel 3D deep convolutional neural network (DCNN) that directly assimilates the hyperspectral data. Furthermore, we interrogate the learnt model to produce physiologically meaningful explanations. We focus on an economica

Clinical prediction rules (CPRs) that predict the absolute risk of a clinical condition or future outcome for individual patients are abundant in the medical literature; however, systematic reviews have demonstrated shortcomings in the methodological quality and reporting of prediction studies. To maximise the potential and clinical usefulness of CPRs, they must be rigorously developed and validated, and their impact on clinical practice and patient outcomes must be evaluated. This review aims t

As organizations increasingly claim to have become more sustainable and to have contributed to global sustainable development, demands for Human Resource Management (HRM) to become sustainable intensify. In the past decade, the concept of Sustainable HRM received increasing attention in both practice and research. However, academics' views about what Sustainable HRM means are diverse, and the effectiveness of Sustainable HRM practices is uncertain. We reviewed key articles in the literature on S

The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framew

IMPORTANCE: Deep learning convolutional neural networks (CNNs) have shown a performance at the level of dermatologists in the diagnosis of melanoma. Accordingly, further exploring the potential limitations of CNN technology before broadly applying it is of special interest. OBJECTIVE: To investigate the association between gentian violet surgical skin markings in dermoscopic images and the diagnostic performance of a CNN approved for use as a medical device in the European market. DESIGN AND SET

BACKGROUND: Interest in value-based healthcare, generally defined as providing better care at lower cost, has grown worldwide, and learning health systems (LHSs) have been proposed as a key strategy for improving value in healthcare. LHSs are emerging around the world and aim to leverage advancements in science, technology and practice to improve health system performance at lower cost. However, there remains much uncertainty around the implementation of LHSs and the distinctive features of thes

BACKGROUND: Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level precision. However, a statistically significant improvement compared with dermatologist classification has not been reported to date. METHODS: For this comparative study, 4204 biopsy-proven images of melanoma and nevi (1:1) were used for the training of a

IMPORTANCE: Offline automated analysis of retinal images on a smartphone may be a cost-effective and scalable method of screening for diabetic retinopathy; however, to our knowledge, assessment of such an artificial intelligence (AI) system is lacking. OBJECTIVE: To evaluate the performance of Medios AI (Remidio), a proprietary, offline, smartphone-based, automated system of analysis of retinal images, to detect referable diabetic retinopathy (RDR) in images taken by a minimally trained health c

BACKGROUND: Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction. METHODS AND RESULTS: We pre

Well-founded data management systems are of vital importance for ocean observing systems as they ensure that essential data are not only collected but also retained and made accessible for analysis and application by current and future users. Effective data management requires collaboration across activities including observations, metadata and data assembly, quality assurance and control (QA/QC), and data publication that enables local and interoperable discovery and access, and secure archivin

BACKGROUND: Eating disorders among adolescents are an ongoing public health concern. Sustainable health promotion programmes require a thorough understanding of the social context in which minors engage. Initial studies show that young people make extensive use of social networks in order to exchange experiences and gather information. During this process their (buying) behaviour is significantly affected by so-called influencers. METHODS: The exploratory research studies non-campaign driven hea

As part of its European strategy for Artificial Intelligence (AI), and as a response to the increasing ethical questions raised by this technology, the European Commission established an independent High-Level Expert Group on Artificial Intelligence (AI HLEG) in June 2018. The group was tasked to draft two deliverables: AI Ethics Guidelines and Policy and Investment Recommendations. Nine months later, its first deliverable was published, putting forward a comprehensive framework to achieve “Trus