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:Denuders.jpg|200px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Čartolovni DigitalHealth2023 9.jpeg|240px]]</div>
A '''[[denuder]]''' is a cylindrical or annular conduit or tube internally coated with a reagent that selectively reacts with a stable flow of gas drawn through the conduit. The gas molecules diffuse to the walls while the [[analyte]] contained in the gas is transmitted outwards via laminar flow, collected, and analyzed. Effectiveness of the system depends primarily "on a complete discrimination between the gas species and particulate matter."
'''"[[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]]"'''


Additional non-linear denuder geometries have also been tried with mixed results. Coiled configurations increased collection efficiency but lost larger particulate. A parallel multi-tube diffusion denuder has also been tried and found to increase collection efficiency. Other geometries include honeycombed, annular, and parallel plate. The development of the annular denuder in particular allowed researchers to overcome the inefficiencies of cylindrical denuders, allowing operation at larger flow rates (up to 30 times that of cylindrical denuders), shorter sampling periods, and less particle loss. ('''[[Denuder|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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''Recently featured'':
''Recently featured'': [[Infectious disease informatics]], [[National Institute for Occupational Safety and Health]], [[Ambulatory surgery center]]
{{flowlist |
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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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