A Boveri perspective on cancer biomarker testing using artificial intelligence.

Authors

Esther Conde, Susana Hernandez, Marta Alonso, Daniel Curto, Fernando Lopez-Rios

Published in

NPJ precision oncology. Volume 10. Issue 1. Sep 08, 2026. Epub Sep 08, 2026.

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.

PMID:
42728348
Bibliographic data and abstract were imported from PubMed on 12 Sep 2026.

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