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10 results on '"dos Santos, Daniel"'

3. CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients

Author

Bernatz, Simon, Elenberger, Oleg, Ackermann, Jörg, Lenga, Lukas, Martin, Simon S., Scholtz, Jan-Erik, Koch, Vitali, Grünewald, Leon D., Herrmann, Yannis, Kinzler, Maximilian N., Stehle, Angelika, Koch, Ina, Zeuzem, Stefan, Bankov, Katrin, Doering, Claudia, Reis, Henning, Flinner, Nadine, Schulze, Falko, Wild, Peter J., Hammerstingl, Renate, Eichler, Katrin, Gruber-Rouh, Tatjana, Vogl, Thomas J., dos Santos, Daniel Pinto, and Mahmoudi, Scherwin

Published
2023
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8. The impact of AI suggestions on radiologists' decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination.

Author

Rezazade Mehrizi, Mohammad H., Mol, Ferdinand, Peter, Marcel, Ranschaert, Erik, Dos Santos, Daniel Pinto, Shahidi, Ramin, Fatehi, Mansoor, and Dratsch, Thomas

Subjects
ATTITUDES toward technology, RADIOLOGISTS, MAMMOGRAMS, ARTIFICIAL intelligence, PILOT projects
Abstract

Various studies have shown that medical professionals are prone to follow the incorrect suggestions offered by algorithms, especially when they have limited inputs to interrogate and interpret such suggestions and when they have an attitude of relying on them. We examine the effect of correct and incorrect algorithmic suggestions on the diagnosis performance of radiologists when (1) they have no, partial, and extensive informational inputs for explaining the suggestions (study 1) and (2) they are primed to hold a positive, negative, ambivalent, or neutral attitude towards AI (study 2). Our analysis of 2760 decisions made by 92 radiologists conducting 15 mammography examinations shows that radiologists' diagnoses follow both incorrect and correct suggestions, despite variations in the explainability inputs and attitudinal priming interventions. We identify and explain various pathways through which radiologists navigate through the decision process and arrive at correct or incorrect decisions. Overall, the findings of both studies show the limited effect of using explainability inputs and attitudinal priming for overcoming the influence of (incorrect) algorithmic suggestions. [ABSTRACT FROM AUTHOR]

Published
2023
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