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A Boveri perspective on cancer biomarker testing using artificial intelligence
Esther Conde1, Susana Hernandez2, Marta Alonso2
1Department of Pathology, Hospital Universitario 12 de Octubre, Universidad Complutense de Madrid, Molecular and Computational Pathology Group, Research Institute Hospital 12 de Octubre (imas12), CIBERONC, Madrid, Spain.
Abstract:
Artificial intelligence (AI) can predict genomic alterations from histology, yet its adoption is slowed by a lack of trust. We argue that deliberate morphology (i.e., a cognitive understanding of histological features supported by standardized annotations) creates a bidirectional feedback loop between clinical practice and model outputs.We translate these observations into an actionable hypothesis for clinical and computational teams: that by enhancing explainability, deliberate morphology could facilitate the responsible deployment of AI biomarkers in oncology.
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