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:Fig1 Guo JofPathInformatics2016 7.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Čartolovni DigitalHealth2023 9.jpeg|240px]]</div>
'''"[[Journal:Digital pathology and anatomic pathology laboratory information system integration to support digital pathology sign-out|Digital pathology and anatomic pathology laboratory information system integration to support digital pathology sign-out]]"'''
'''"[[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 adoption of digital pathology offers benefits over labor-intensive, time-consuming, and error-prone manual processes. However, because most workflow and [[laboratory]] transactions are centered around the anatomical pathology laboratory information system (APLIS), adoption of digital pathology ideally requires integration with the APLIS. A digital pathology system (DPS) integrated with the APLIS was recently implemented at our institution for diagnostic use. We demonstrate how such integration supports digital workflow to sign-out anatomical pathology cases.  
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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Workflow begins when pathology cases get accessioned into the APLIS ([[Cerner Corporation|CoPathPlus]]). Glass slides from these cases are then digitized (Omnyx VL120 scanner) and automatically uploaded into the DPS (Omnyx; Integrated Digital Pathology (IDP) software v.1.3). The APLIS transmits case data to the DPS via a publishing web service. The DPS associates scanned images with the correct case using barcode labels on slides and information received from the APLIS. When pathologists remotely open a case in the DPS, additional information (e.g. gross pathology details, prior cases) gets retrieved from the APLIS through a query web service. ('''[[Journal:Digital pathology and anatomic pathology laboratory information system integration to support digital pathology sign-out|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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