Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig6 Argento EMBOReports2020 21-3.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Čartolovni DigitalHealth2023 9.jpeg|240px]]</div>
'''"[[Journal:Institutional ELN-LIMS deployment: Highly customizable ELN-LIMS platform as a cornerstone of digital transformation for life sciences research institutes|Institutional ELN-LIMS deployment: Highly customizable ELN-LIMS platform as a cornerstone of digital transformation for life sciences research institutes]]"'''
'''"[[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]"'''


The systematic recording and management of experimental data in academic life science research remains an open problem. École Polytechnique Fédérale de Lausanne (EPFL) engaged in a program of deploying both an [[electronic laboratory notebook]] (ELN) and a [[laboratory information management system]] (LIMS) six years ago, encountering a host of fundamental questions at the institutional level and within each [[laboratory]]. Here, based on our experience, we aim to share with research institute managers, principal investigators (PIs), and any scientists involved in a combined ELN-LIMS deployment helpful tips and tools, with a focus on surrounding yourself with the right people and the right software at the right time. In this article we describe the resources used, the challenges encountered, key success factors, and the results obtained at each phase of our project. Finally, we discuss the current and next challenges we face, as well as how our experience leads us to support the creation of a new position in the research group: the laboratory data manager. ('''[[Journal:Institutional ELN-LIMS deployment: Highly customizable ELN-LIMS platform as a cornerstone of digital transformation for life sciences research institutes|Full article...]]''')<br />
This qualitative study aims to present the aspirations, expectations, and critical analysis of the potential for [[artificial intelligence]] (AI) to transform the patient–physician relationship, according to multi-stakeholder insight. This study was conducted from June to December 2021, using an anticipatory ethics approach and sociology of expectations as the theoretical frameworks. It focused mainly on three groups of stakeholders, namely physicians (''n'' = 12), patients (''n'' = 15), and healthcare managers (''n'' = 11), all of whom are directly related to the adoption of AI in medicine (''n'' = 38). In this study, interviews were conducted with 40% of the patients in the sample (15/38), as well as 31% of the physicians (12/38) and 29% of health managers in the sample (11/38) ... ('''[[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study|Full article...]]''')<br />
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Latest revision as of 15:48, 26 May 2024

Fig1 Čartolovni DigitalHealth2023 9.jpeg

"Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study"

This qualitative study aims to present the aspirations, expectations, and critical analysis of the potential for artificial intelligence (AI) to transform the patient–physician relationship, according to multi-stakeholder insight. This study was conducted from June to December 2021, using an anticipatory ethics approach and sociology of expectations as the theoretical frameworks. It focused mainly on three groups of stakeholders, namely physicians (n = 12), patients (n = 15), and healthcare managers (n = 11), all of whom are directly related to the adoption of AI in medicine (n = 38). In this study, interviews were conducted with 40% of the patients in the sample (15/38), as well as 31% of the physicians (12/38) and 29% of health managers in the sample (11/38) ... (Full article...)
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