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J. Ammeling

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#human-computer interacti... Open access Mar 2026

Stuck on Suggestions: Automation Bias, the Anchoring Effect, and the Factors That Shape Them in Computational Pathology

The findings indicate that AI integration generally enhances diagnostic performance, but also introduced a 7% automation bias rate, quantified as the number of accepted negative consultations, where a previously correct independent assessment gets overturned by inaccurate AI guidance.

Emely Rosbach, J. Ammeling, J. Ganz et al. · 6 citations · ⚡1
Review Open access Aug 2026

Cognitive biases in AI-assisted medical decision making: A structured review as a primer for veterinary and human pathology.

This review aims to identify cognitive biases and their modes of manifestation in human-computer interaction within pathology and to extrapolate potential manifestations of biases not yet explored in this domain but reported in HCIs across other medical specialties.

Emely Rosbach, J. Ammeling, Christof A. Bertram et al. · 0 citations

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