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

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'''"[[Journal:Global data quality assessment and the situated nature of “best” research practices in biology|Global data quality assessment and the situated nature of “best” research practices in biology]]"'''
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Čartolovni DigitalHealth2023 9.jpeg|240px]]</div>
'''"[[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]]"'''


This paper reflects on the relation between international debates around data quality assessment and the diversity characterizing research practices, goals and environments within the life sciences. Since the emergence of molecular approaches, many biologists have focused their research, and related methods and instruments for data production, on the study of genes and genomes. While this trend is now shifting, prominent institutions and companies with stakes in molecular biology continue to set standards for what counts as "good science" worldwide, resulting in the use of specific data production technologies as proxy for assessing data quality. This is problematic considering (1) the variability in research cultures, goals and the very characteristics of biological systems, which can give rise to countless different approaches to knowledge production; and (2) the existence of research environments that produce high-quality, significant datasets despite not availing themselves of the latest technologies. Ethnographic research carried out in such environments evidences a widespread fear among researchers that providing extensive information about their experimental set-up will affect the perceived quality of their data, making their findings vulnerable to criticisms by better-resourced peers. These fears can make scientists resistant to sharing data or describing their provenance. ('''[[Journal:Global data quality assessment and the situated nature of “best” research practices in biology|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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