17 research outputs found

    Unterschiede im Interessenprofil von Studenten der Ergotherapie und Logopädie

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    Das primäre Ziel dieser Diplomarbeit ist es, die Aussagekraft des neu entwickelten Interessenfragebogen RIASEC-RRK, Version 2 (Arendasy et al., 2007) anhand der Stichproben der Logopäden und der Ergotherapeuten zu erweitern. Gleichzeitig werden Interessenschwerpunkte beider Studienrichtungen erarbeitet und signifikante Unterschiede in den Interessensprofilen der beiden Studienrichtungen ermittelt. Bei beiden Studienrichtungen dominieren die sozialen Interessen (S), gefolgt von den künstlerisch-sprachlichen Interessen (A) und als drittstärkstes Interesse der wissenschaftlich, forschende Bereich (I). Weiters zeigen Ergotherapie-Studenten ein signifikant höheres praktisch -technisches Interesse, Logopädie-Studenten hingegen ein signifikant höheres konventionelles Interesse. In den Dimensionen: „Investigative“, „Artistic“, „Social“ und „Enterprising“ zeigen sich keine signifikanten Unterschiede zwischen Ergotherapie- und Logopädie-Studenten. Für eine bestmögliche Entscheidung bezüglich der Studienwahl, ist es jedoch wichtig, dass in der Studienberatung neben den Ergebnissen dieser Diplomarbeit auch weitere Informationen, wie Aufgabenbereiche, Beschäftigungsmöglichkeiten und Arbeitsfelder, mit einbezogen werden.The aim of this study is to enlarge the meaningfulness of the new developed interest questionnaire RIASEC-RRK, Version 2 (Arendasy et al., 2007) with the sample of students of occupational therapy and logopedia and to investigate the differences in Holland´s (1997) interest types (realistic, investigative, artistic, social, enterprising and conventional). 110 students completed the online questionnaire which has a total of 126 questions. For both studies a three-dimensional interest structure can be specified: the social interest dominates, followed by the artistic and the investigative interest. Students of occupational therapy show higher realistic interests and the students of logopedia demonstrate higher conventional interests. This research has identified personal characteristics that can be added to current selection criteria to assist in identifying suitable candidates for occupational therapy and logopedia education

    FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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    Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

    Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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    Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

    FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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    Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

    FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

    Get PDF
    Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

    Fear of Influenza Resurgence amid COVID-19 Pandemic: Need for Effective Flu Vaccine Still Exists

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    As influenza season was approaching in 2020, public health officials feared that influenza would worsen the COVID-19 situation [...

    IManageCancer: developing a platform for empowering patients and strengthening self-management in cancer diseases

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    Cancer research has led to more cancer patients being cured, and many more enabled to live with their cancer. As such, some cancers are now considered a chronic disease, where patients and their families face the challenge to take an active role in their own care and in some cases in their treatment. To this direction the iManageCancer project aims to provide a cancer specific self-management platform designed according to the needs of patient groups while focusing, in parallel, on the wellbeing of the cancer patient. In this paper, we present the use-case requirements collected using a survey, a workshop and the analysis of three white papers and then we explain the corresponding system architecture. We describe in detail the main technological components of the designed platform, show the current status of development and we discuss further directions of research

    SFN action impacts HIV-2 as well as HIV-1 and is not reporter dependent.

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    <p>PMA-differentiated THP1 cells were treated with media supplemented with vehicle only (DMSO), 5 μM AZT or with 10 μM SFN. Twenty-four hours after treatment, the samples were either mock infected or infected with (A), VSV-G pseudotyped HIV-1 encoding firefly luciferase in place of <i>nef</i> or (B), VSV-G pseudotyped HIV-2 encoding firefly luciferase in place of <i>nef</i>. Twenty-four hours after infection, luciferase activity was measured by photon emission. (C), In parallel, the same experiment as in (A) and (B) was performed except that THP1 cells were infected with VSV-G-pseudotyped HIV-1 with GFP in place of <i>nef</i> or (D), VSV-G pseudotyped HIV-2 with GFP in place of <i>nef</i>. The samples with GFP-reporter viruses were fixed and harvested 24 h after infection and the fraction of GFP(+) cells was enumerated by flow cytometry. Bar graphs represent the data for replicate experiments (n = 3).</p
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