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:Fig2 DiNardo Toxins2020 12-4.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Čartolovni DigitalHealth2023 9.jpeg|240px]]</div>
'''"[[Journal:Enzyme immunoassay for measuring aflatoxin B1 in legal cannabis|Enzyme immunoassay for measuring aflatoxin B1 in legal cannabis]]"'''
'''"[[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 diffusion of the legalization of [[wikipedia:Cannabis|cannabis]] for recreational, medicinal, and nutraceutical uses requires the development of adequate analytical methods to assure the safety and security of such products. In particular, [[wikipedia:Aflatoxin|aflatoxins]] are considered to pose a major risk for the health of cannabis consumers. Among analytical methods that allow for adequate monitoring of food safety, [[immunoassay]]s play a major role thanks to their cost-effectiveness, high-throughput capacity, simplicity, and limited requirement for equipment and skilled operators. Therefore, a rapid and sensitive [[enzyme immunoassay]] has been adapted to measure the most hazardous [[wikipedia:Aflatoxin B1|aflatoxin B<sub>1</sub>]] in cannabis products. The assay was acceptably accurate (recovery rate: 78–136%), reproducible (intra- and inter-assay means coefficients of variation 11.8% and 13.8%, respectively), and sensitive (limit of detection and range of quantification: 0.35 ng mL<sup>−1</sup> and 0.4–2 ng mL<sup>−1</sup> ... ('''[[Journal:Enzyme immunoassay for measuring aflatoxin B1 in legal cannabis|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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