The role of ethics in science: a systematic literature review from the first wave of COVID-19

This paper proposes a systematic literature review on ethics and CoviD-19, aiming to understand the impact and the perception of the pandemic during the first wave (January-June 2020) and the consequences one year later. PubMed was systematically searched up May 2020 to identify studies that took into consideration various ethical issues that have been arising from the Covid-19 outbreak. The eligibility of the papers was determined by two authors, who screened the results mediated by a third author. In order to facilitate the screening, the titles were divided into five sub-thematic macro-areas, namely allocation, policy, specialist, clinical trials, and technology and, when possible, per geographical area. Specifically, a posteriori, we decided to focus on the papers referring to policies and technology, as they highlighted ethical issues that are not overused and worthy of particular attention. Thus, 38 studies out of 233 met our inclusion criteria and were fully analysed. Accordingly, this review touches on themes such as fairness, equity, transparency of information, the duty of care, racial disparities, the marginalisation of the poor, and privacy and ethical concerns. Overall, it was found that despite the increased awareness of interdisciplinarity and the essential reference to ethics, many scientific articles use it with little competence, considering it only a "humanitarian" enrichment. In fact, as we understand, reflecting a year after the outbreak of the pandemic, although Covid-19 is leading scientists to increasingly recognise the importance of ethical issues, there is still a lot of confusion that could be helped by establishing international guidelines to act as a moral compass in times of crisis.Supplementary informationThe online version contains supplementary material available at 10.1007/s12553-021-00570-6

La formazione degli insegnanti di sostegno sulle TIC. Analisi dei prodotti multimediali del corso di specializzazione per le attività di sostegno

ICT has been introduced in the Italian school system for more than 20 years (National Informatics Plan, 1985; 1991, National Digital School Plan, 2007; 2015) in order to facilitate teaching-learning processes for all. Researches show that besides access to technology, ICT-related educational innovation success depends above all on teacher training.In Italy, specialized teaching courses (Ministerial Decree 30/09/2011) prepare prospective teachers for working in inclusive classroom. Within these courses, a 75-hours class provides training for using ICT in the educational processes. The creation of a multimedia product is one of the demands of the final assessment.On the basis of the analysis of multimedia products presented by the prospective teachers who attended the course at University of Rome “Foro Italico” (2014/2015), the present work aims to highlight good practices but also critical aspects in SEN teachers training on ICT in order to reflect on how to improve training efficacy and impact

The use of smart environments and robots for infection prevention control : a systematic literature review

Infection prevention and control (IPC) is essential to prevent nosocomial infections. The implementation of automation technologies can aid outbreak response. This manuscript aims at investigating the current use and role of robots and smart environments on IPC systems in nosocomial settings. The systematic literature review was performed following the PRISMA statement. Literature was searched for articles published in the period January 2016 to October 2022. Two authors determined the eligibility of the papers, with conflicting decisions being mitigated by a third. Relevant data was then extracted using an ad-hoc extraction table to facilitate the analysis and narrative synthesis. The quality of the included studies was appraised by two authors. The search strategy returned 1520 citations and 17 papers were included in this review. This review identified three main areas of interest: hand hygiene and personal protective equipment compliance, automatic infection cluster detection and environments cleaning (i.e., air quality control, sterilization). This review demonstrates that IPC practices within hospitals mostly do not rely on automation and robotic technology, and few advancements have been made in this field. Increasing the awareness of health care workers on these technologies, through training and involving them in the design process, is essential to accomplish the Health 4.0 transformation. Research priorities should also be considering how to implement similar or more contextualized alternatives for low-income countries

The use of artificial intelligence systems in diagnosis of pneumonia via signs and symptoms : a systematic review

Artificial Intelligence (AI) systems using symptoms/signs to detect respiratory diseases may improve diagnosis especially in limited resource settings. Heterogeneity in such AI systems creates an ongoing need to analyse performance to inform future research. This systematic literature review aimed to investigate performance and reporting of diagnostic AI systems using machine learning (ML) for pneumonia detection based on symptoms and signs, and to provide recommendations on best practices for designing and implementing predictive ML algorithms. This article was conducted following the PRISMA protocol, 876 articles were identified by searching PubMed, Scopus, and OvidSP databases (last search 5th May 2021). For inclusion, studies must have differentiated clinically diagnosed pneumonia from controls or other diseases using AI. Risk of Bias was evaluated using The STARD 2015 tool. Information was extracted from 16 included studies regarding study characteristics, ML-model features, reference tests, study population, accuracy measures and ethical aspects. All included studies were highly heterogenous concerning the study design, setting of diagnosis, study population and ML algorithm. Study reporting quality in methodology and results was low. Ethical issues surrounding design and implementation of the AI algorithms were not well explored. Although no single performance measure was used in all studies, most reported an accuracy measure over 90%. There is strong evidence to support further investigations of ML to automatically detect pneumonia based on easily recognisable symptoms and signs. To help improve the efficacy of future research, recommendations for designing and implementing AI tools based on the findings of this study are provided

N-Beats as an EHG signal forecasting method for labour prediction in full term pregnancy

The early prediction of onset labour is critical for avoiding the risk of death due to pregnancy delay. Low-income countries often struggle to deliver timely service to pregnant women due to a lack of infrastructure and healthcare facilities, resulting in pregnancy complications and, eventually, death. In this regard, several artificial-intelligence-based methods have been proposed based on the detection of contractions using electrohysterogram (EHG) signals. However, the forecasting of pregnancy contractions based on real-time EHG signals is a challenging task. This study proposes a novel model based on neural basis expansion analysis for interpretable time series (N-BEATS) which predicts labour based on EHG forecasting and contraction classification over a given time horizon. The publicly available TPEHG database of Physiobank was exploited in order to train and test the model, where signals from full-term pregnant women and signals recorded after 26 weeks of gestation were collected. For these signals, the 30 most commonly used classification parameters in the literature were calculated, and principal component analysis (PCA) was utilized to select the 15 most representative parameters (all the domains combined). The results show that neural basis expansion analysis for interpretable time series (N-BEATS) forecasting can forecast EHG signals through training after few iterations. Similarly, the forecasting signal’s duration is determined by the length of the recordings. We then deployed XG-Boost, which achieved the classification accuracy of 99 percent, outperforming the state-of-the-art approaches using a number of classification features greater than or equal to 15

GATEKEEPER’s Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases

Background: The World Health Organization’s strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs. Objective: We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform. Methods: The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities. Results: Seven European countries were selected, covering Europe’s geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence–based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors. Conclusions: This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space

Biomedical and clinical engineering contribution in WHO response for Covid-19 pandemic

For the first time after decades, the Covid-19 pandemic exposed to a scenario of limited resources also high-income countries such as Europe or USA. This made even more clear the importance of disaster preparedness and responsible innovation. After providing a quick summary of the World Health Organization (WHO) response to the Covid-19, this chapiter introduces the concept of preparedness, leading the readers into the USA Centre for Disease Control (CDC) Hierarchy of Control model applied to Covid-19, and the need for medical intelligence in order to prevent future disasters. The chapiter focuses on the WHO priorities for innovations, analysing the limits of regulatory frameworks and international standards for medical devices and PPE. Finally, the last section reports few considerations about ethical issues as faced by the authors

Analysing Italian Inclusive Education Practices in Relation to Universal Design for Learning Principles

: This study aims to investigate how teaching practices in the Italian inclusive education system align with the principles of Universal Design for Learning (UDL), which is aimed at providing access to education for all students, including those with disabilities. In line with Article 2 of the Convention on the Rights of Persons with Disabilities (CRPD), which requires states to promote Universal Design (UD) in all aspects of life, including education, this research examines the extent to which the Italian education system meets this requirement. The study involved teachers who participated in a course on inclusive education. The research was conducted in three phases, including the introduction of UDL, identification of teachers' perceptions and initial reactions to UDL, compilation of a questionnaire related to UDL checkpoints, and a focus group discussion on teachers' attitudes towards UDL and the use of information and communication technologies (ICTs) in the classroom. The analysis focused on the first UDL principle, "Provide Multiple Means of Representation," which emphasizes the need to present information in an accessible way to learners with disabilities. The findings revealed that despite not having previous training on UDL, teachers in the Italian inclusive education system use ICTs in their daily teaching practices to make knowledge accessible, which is in line with the UDL principles. However, the study also highlighted a lack of awareness and reflection on the use of ICTs in teaching, suggesting the need for specific training to enhance inclusive practices. This study contributes to the ongoing dialogue on inclusive education in Italy and highlights the importance of promoting UD principles in education to ensure that all learners, regardless of their abilities, have equal access to education. Furthermore, it underscores the significance of providing adequate training and support to teachers to facilitate inclusive practices and improve learning outcomes for all students

Building Policies and Initiatives for Inclusive Educational Contexts: The GLIC Italian Experience

: Inclusive education has emerged as a global priority, and the integration of assistive technology (AT) is recognized as a crucial component for creating inclusive educational environments. However, the successful implementation of AT hinges on supportive policies and initiatives. This article delves into the experience of the GLIC Association in collaboration with the Italian Ministry of Education, exploring their efforts in developing policies and initiatives to facilitate the introduction of AT in educational contexts. The GLIC Association has devised a service provisioning model in state schools that ensures adequate support for the integration of AT, thus promoting inclusive education

Deep-Learning-Driven Techniques for Real-Time Multimodal Health and Physical Data Synthesis

With the advent of Artificial Intelligence for healthcare, data synthesis methods present crucial benefits in facilitating the fast development of AI models while protecting data subjects and bypassing the need to engage with the complexity of data sharing and processing agreements. Existing technologies focus on synthesising real-time physiological and physical records based on regular time intervals. Real health data are, however, characterised by irregularities and multimodal variables that are still hard to reproduce, preserving the correlation across time and different dimensions. This paper presents two novel techniques for synthetic data generation of real-time multimodal electronic health and physical records, (a) the Temporally Correlated Multimodal Generative Adversarial Network and (b) the Document Sequence Generator. The paper illustrates the need and use of these techniques through a real use case, the H2020 GATEKEEPER project of AI for healthcare. Furthermore, the paper presents the evaluation for both individual cases and a discussion about the comparability between techniques and their potential applications of synthetic data at the different stages of the software development life-cycle