Additional file 2 of Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system

Additional file 2. User study questionnaire administered to pathologists. Content of the user study questionnaire administered to pathologists to evaluate the integration framework in terms of user-friendliness and potential effectiveness. Mandatory fields are marked with an asterisk (*).

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Publication Details

Journal
Figshare
Published
2025-01-01
DOI
https://doi.org/10.6084/m9.figshare.29151426
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Additional file 2 of Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system

Figshare
AI in cancer detection
article

Additional file 2 of Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system

article en

Abstract

Additional file 2. User study questionnaire administered to pathologists. Content of the user study questionnaire administered to pathologists to evaluate the integration framework in terms of user-friendliness and potential effectiveness. Mandatory fields are marked with an asterisk (*).

Figshare
University of Molise (IT), University of Salerno (IT), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Comprehensive Cancer Center Erlangen (DE), Azienda Ospedaliera Universitaria Pisana (IT)
Openalex Percentile: Top 74%
AI in cancer detection
DOIOpen Access PDFOpenAlex
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Additional file 2 of Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system · Figshare (2025) | TGRS Research Map | TGRS