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    <pageNumber>18</pageNumber>
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    <articleNumber>528</articleNumber>
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    <publisherName>ACM</publisherName>
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    <completedDate>2025-04-29</completedDate>
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    <title language="eng">"When Two Wrongs Don't Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology</title>
    <abstract language="eng">Artificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration can introduce and amplify cognitive biases, like confirmation bias caused by false confirmation when erroneous human opinions are reinforced by inaccurate AI output. This bias may increase under time pressure, a ubiquitous factor in routine pathology, as it strains practitioners’ cognitive resources. We quantified confirmation bias triggered by AI-induced false confirmation and examined the role of time constraints in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results suggest that AI integration fuels confirmation bias, evidenced by a statistically significant positive linear-mixed-effects model coefficient linking AI recommendations mirroring flawed human judgment and alignment with system advice. Conversely, time pressure appeared to weaken this relationship. These findings highlight potential risks of AI in healthcare and aim to support the safe integration of clinical decision support systems.</abstract>
    <parentTitle language="eng">CHI'25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems</parentTitle>
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      <first_name>Mun Keong</first_name>
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      <first_name>Alexander F. H.</first_name>
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      <first_name>Ronald R.</first_name>
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      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
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  <doc>
    <id>5969</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
    <pageNumber>16</pageNumber>
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    <completedDate>2025-06-10</completedDate>
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    <title language="eng">"When TwoWrongs Don’t Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology</title>
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      <first_name>Alexander F. H.</first_name>
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  <doc>
    <id>1181</id>
    <completedYear>2020</completedYear>
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    <language>eng</language>
    <pageFirst>214</pageFirst>
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    <pageNumber/>
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    <issue>2</issue>
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    <publisherName>Sage</publisherName>
    <publisherPlace>London</publisherPlace>
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    <contributingCorporation/>
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    <completedDate>2022-02-10</completedDate>
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    <title language="eng">Computerized Calculation of Mitotic Count Distribution in Canine Cutaneous Mast Cell Tumor Sections: Mitotic Count Is Area Dependent</title>
    <parentTitle language="eng">Veterinary Pathology</parentTitle>
    <identifier type="issn">1544-2217</identifier>
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    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1177/0300985819890686</enrichment>
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    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Corinne</first_name>
      <last_name>Gurtner</last_name>
    </author>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Bartel</last_name>
    </author>
    <author>
      <first_name>Sarah M.</first_name>
      <last_name>Corner</last_name>
    </author>
    <author>
      <first_name>Martina</first_name>
      <last_name>Dettwiler</last_name>
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    <author>
      <first_name>Olivia</first_name>
      <last_name>Kershaw</last_name>
    </author>
    <author>
      <first_name>Erica L.</first_name>
      <last_name>Noland</last_name>
    </author>
    <author>
      <first_name>Anja</first_name>
      <last_name>Schmidt</last_name>
    </author>
    <author>
      <first_name>Dodd G.</first_name>
      <last_name>Sledge</last_name>
    </author>
    <author>
      <first_name>Rebecca C.</first_name>
      <last_name>Smedley</last_name>
    </author>
    <author>
      <first_name>Tuddow</first_name>
      <last_name>Thaiwong</last_name>
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    <author>
      <first_name>Matti</first_name>
      <last_name>Kiupel</last_name>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>area selection</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>high-power field</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mitotic activity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mitotic figure distribution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>tumor grading</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>tumor periphery</value>
    </subject>
  </doc>
  <doc>
    <id>1314</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>211</pageFirst>
    <pageLast>226</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>59</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Sage</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy</title>
    <abstract language="eng">The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs.</abstract>
    <parentTitle language="eng">Veterinary Pathology</parentTitle>
    <identifier type="issn">1544-2217</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-13141</identifier>
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    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
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    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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    <author>
      <first_name>Taryn</first_name>
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      <first_name>Alexander</first_name>
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      <first_name>Frauke</first_name>
      <last_name>Wilm</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
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    <author>
      <first_name>Charles-Antoine</first_name>
      <last_name>Assenmacher</last_name>
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    <author>
      <first_name>Kathrin</first_name>
      <last_name>Becker</last_name>
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    <author>
      <first_name>Mark</first_name>
      <last_name>Bennett</last_name>
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    <author>
      <first_name>Sarah M.</first_name>
      <last_name>Corner</last_name>
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    <author>
      <first_name>Brieuc</first_name>
      <last_name>Cossic</last_name>
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      <first_name>Daniela</first_name>
      <last_name>Denk</last_name>
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      <first_name>Martina</first_name>
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    <author>
      <first_name>Beatriz</first_name>
      <last_name>Garcia Gonzalez</last_name>
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    <author>
      <first_name>Corinne</first_name>
      <last_name>Gurtner</last_name>
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    <author>
      <first_name>Ann-Kathrin</first_name>
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      <first_name>Annabelle</first_name>
      <last_name>Heier</last_name>
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      <first_name>Annika</first_name>
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      <first_name>Sophie</first_name>
      <last_name>Merz</last_name>
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</export-example>
