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Designing for Qualitative Evaluation of Synthetic Medical Data

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Title: Designing for Qualitative Evaluation of Synthetic Medical Data
Authors: Silva,Isabella Barbosa, Oliveira,Elsa, Melo,Ricardo, Rosado,Luís, Gálvez-Barrón,César, Heijink, Irene Bernadet, Hoogteijling, Sem, Gabilondo,Iñigo, Projectafdeling FNE, KNF
Publication Year: 2025
Subject Terms: Doctor-in-the-Loop (DITL), Human-Computer Interaction (HCI), Machine Learning in Healthcare, Participatory Design, Qualitative Evaluation, Synthetic Data (SD), Synthetic medical data (SMD), Taverne, Human-Computer Interaction, Computer Graphics and Computer-Aided Design, Software
Description: Machine learning in healthcare often struggles with data access for model training due to privacy restrictions, rare conditions, and high acquisition costs. Synthetic data offers a potential workaround, yet there are no agreed-upon gold standards for evaluating it. As quantitative metrics alone cannot fully assess the desired qualities of generative model outputs, human inspection is a key component of validation, warranting a “Doctor-in-the-loop” approach. However, research is scarce on best practices for interaction and user interface design in such systems. This paper presents preliminary designs for qualitative synthetic medical data evaluation, informed by four participatory workshops with seven doctors and nine machine learning engineers. Spanning tabular, image, and time series data, this study emphasised transparency and clear communication of the synthetic data generation. In addition to presenting the rationale behind the evaluation workflow design, we highlight challenges in the medical domain, including doctors’ limited familiarity and skepticism with synthetic data.
Document Type: book part
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Language: English
Relation: https://dspace.library.uu.nl/handle/1874/461756
Availability: https://dspace.library.uu.nl/handle/1874/461756
Rights: info:eu-repo/semantics/OpenAccess
Accession Number: edsbas.9FEEB952
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  Data: Designing for Qualitative Evaluation of Synthetic Medical Data
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  Data: <searchLink fieldCode="AR" term="%22Silva%2CIsabella+Barbosa%22">Silva,Isabella Barbosa</searchLink><br /><searchLink fieldCode="AR" term="%22Oliveira%2CElsa%22">Oliveira,Elsa</searchLink><br /><searchLink fieldCode="AR" term="%22Melo%2CRicardo%22">Melo,Ricardo</searchLink><br /><searchLink fieldCode="AR" term="%22Rosado%2CLuís%22">Rosado,Luís</searchLink><br /><searchLink fieldCode="AR" term="%22Gálvez-Barrón%2CCésar%22">Gálvez-Barrón,César</searchLink><br /><searchLink fieldCode="AR" term="%22Heijink%2C+Irene+Bernadet%22">Heijink, Irene Bernadet</searchLink><br /><searchLink fieldCode="AR" term="%22Hoogteijling%2C+Sem%22">Hoogteijling, Sem</searchLink><br /><searchLink fieldCode="AR" term="%22Gabilondo%2CIñigo%22">Gabilondo,Iñigo</searchLink><br /><searchLink fieldCode="AR" term="%22Projectafdeling+FNE%22">Projectafdeling FNE</searchLink><br /><searchLink fieldCode="AR" term="%22KNF%22">KNF</searchLink>
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  Data: 2025
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  Data: <searchLink fieldCode="DE" term="%22Doctor-in-the-Loop+%28DITL%29%22">Doctor-in-the-Loop (DITL)</searchLink><br /><searchLink fieldCode="DE" term="%22Human-Computer+Interaction+%28HCI%29%22">Human-Computer Interaction (HCI)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+Learning+in+Healthcare%22">Machine Learning in Healthcare</searchLink><br /><searchLink fieldCode="DE" term="%22Participatory+Design%22">Participatory Design</searchLink><br /><searchLink fieldCode="DE" term="%22Qualitative+Evaluation%22">Qualitative Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Synthetic+Data+%28SD%29%22">Synthetic Data (SD)</searchLink><br /><searchLink fieldCode="DE" term="%22Synthetic+medical+data+%28SMD%29%22">Synthetic medical data (SMD)</searchLink><br /><searchLink fieldCode="DE" term="%22Taverne%22">Taverne</searchLink><br /><searchLink fieldCode="DE" term="%22Human-Computer+Interaction%22">Human-Computer Interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Graphics+and+Computer-Aided+Design%22">Computer Graphics and Computer-Aided Design</searchLink><br /><searchLink fieldCode="DE" term="%22Software%22">Software</searchLink>
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  Data: Machine learning in healthcare often struggles with data access for model training due to privacy restrictions, rare conditions, and high acquisition costs. Synthetic data offers a potential workaround, yet there are no agreed-upon gold standards for evaluating it. As quantitative metrics alone cannot fully assess the desired qualities of generative model outputs, human inspection is a key component of validation, warranting a “Doctor-in-the-loop” approach. However, research is scarce on best practices for interaction and user interface design in such systems. This paper presents preliminary designs for qualitative synthetic medical data evaluation, informed by four participatory workshops with seven doctors and nine machine learning engineers. Spanning tabular, image, and time series data, this study emphasised transparency and clear communication of the synthetic data generation. In addition to presenting the rationale behind the evaluation workflow design, we highlight challenges in the medical domain, including doctors’ limited familiarity and skepticism with synthetic data.
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      – Text: English
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      – SubjectFull: Machine Learning in Healthcare
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      – SubjectFull: Participatory Design
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      – TitleFull: Designing for Qualitative Evaluation of Synthetic Medical Data
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