1,720,960 research outputs found
Transformation of health and social chare through autonomous and intelligent systems – Chances and challenges
No full textFor improving quality and safety of healthcare as well as efficiency and efficacy of care processes, health systems turn toward personalized, preventive, predictive, participative precision medicine (5P Medicine). The resulting ecosystem combines different domains represented by a huge variety of different human and non-human actors belonging to different policy domains, coming from different disciplines and so deploying different methodologies, terminologies, and ontologies. They offer different levels of knowledge, skills, and experiences, act in different scenarios and accommodate different business cases for meeting the intended business objectives. In that context, currently practiced data level interoperability has to advance to dynamic and individually tailored knowledge-based cooperation. For correctly modeling and managing such systems and their behavior, a system-oriented, architecture-centric, ontology-based, policy-driven approach, standardized in ISO DIS 23903 Interoperability and Integration Reference Architecture, is inevitable. Requirements and solutions for designing and implementing advanced P5 Medicine ecosystems with reference to related standards, specifications and projects will be discussed in necessary detail
Scientific Session on Thursday, November 14th
Get PDFThe scientific session encompassed a range of topics centred around the application of technology to advance healthcare and medical education.
David Naguib’s presentation explored the potential and challenges of using artificial intelligence (AI) in electronic health record-(EHR-)based pharmacovigilance in Germany and Egypt to improve adverse drug reactions (ADR) detection.
Mariam Barseghyan and colleagues discussed a digital intervention using the ICOnnecta’t App to address the psychological impact of breast cancer and premature menopause.
Tatul Saghatelyan and colleagues presented the successful pilot implementation of a mobile mammographic screening program in Armenia, highlighting its IT infrastructure and impact on early detection.
Arman Darbinyan and colleagues detailed the use of deep learning for automatic electrocardiography (ECG) and mammography analysis to detect cardiovascular diseases and cancer.
Arsen Arakelyan introduced the Armenian Genome Project as a pathway to personalized medicine, focusing on characterizing the Armenian genome and its implications for health.
Finally, Ozar Mintser’s presentation addressed AI-powered learning technologies and the knowledge tracing problem in medical education, advocating for proactive educational strategies.
Overall, the session showcased diverse applications of AI and digital platforms in screening, diagnosis, monitoring, and education across different medical fields and geographical contexts
Analysis of Legal and Regulatory Frameworks in Digital Health: A Comparison of Guidelines and Approaches in the European Union and United States
Get PDFThe advent of digital technology in healthcare presents opportunities for the improvement of healthcare systems around the world and the move towards value-based treatment. However, this move must be accompanied by strong legal and regulatory frameworks that will not only facilitate but encourage the good use of technology. The goal of the study was to assess the amenability and furtherance of regulatory frameworks in digital health by evaluating and comparing the processes, effectiveness and outcomes of these frameworks in the European Union and United States. Methods: This study incorporated two research methodologies. The first was a research of current legal and regulatory frameworks in digital health in the European Union and United States. A comprehensive online search for publications was carried out which included laws, regulations, policies, green papers, guidelines and recommendations. This research was complemented with interviews of five purposively sampled key informants in the legal and regulatory landscape. Results: Mind-maps revealed key features and challenges of the digital health field in the topics of the current state of regulation of digital health in the EU, Germany and US, regulatory pathways for digital health devices, protection and privacy of health data, mobile health validation, risk-based classification of medical devices, regulation of clinical decision support systems, telemedicine, artificial intelligence and emerging technologies, reimbursement for digital health services and liability for digital health products. The experts expressed and explained key points where current regulation is deficient. The review of the legal frameworks revealed deficiencies which provide opportunities and recommendations to further develop and strengthen the regulatory landscape. Conclusions: A key element to a robust regulatory framework is the ability to ensure trust and confidence in using digital health technology. Technology must measure the impact on quality of life and burden of disease and not merely involve the collection of data
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Bias – The Achilles Heel of Artificial Intelligence in Healthcare
No full textThe field of artificial intelligence (AI) has evolved considerably since the end of the 20th century. While this technology shows great promise and potential to solve daily tasks, the question of fairness of decisions by AI models needs to be addressed. There have been examples of AI models performing unfair and prejudiced decisions which has led to a growing need to be able to know ‘why’ and ‘how’ these models make decisions. This is particularly important in the healthcare field, where the outcomes of AI models play a decisive role in the well-being of patients. In addition, a system for detecting and mitigating biases needs to be developed so that the advantages of AI can be utilized in healthcare. A scoping review was carried out to study the source, nature and impact of biases of AI models. Results showed that bias can be data-driven, algorithmic or introduced by humans. These biases propagate deeply rooted societal inequality, misdiagnose patient groups, and further perpetuate global health inequity. Mitigation of biases is proposed at the various stages of the machine learning pipeline. These strategies use techniques such as scrutinizing the way data is collected, better representation of patient groups, optimal training of the model and evaluating model performance. In conclusion, it must be ascertained that AI decisions are free of unwarranted biases and justly fair. Therefore, in an effort to mitigate bias, AI models should adopt systems that contain techniques in which biases can be predicted, measured, explained and then mitigated
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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