Application of artificial intelligence and digital tools in cancer pathology
Artificial intelligence (AI) is on the verge of reshaping cancer diagnostics through integration into digital pathology workflows. Despite the progression of AI towards real-world deployment, challenges in interpretability, validation, and clinical integration persist. AI models support the interpre...
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| Main Authors: | , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
October 2025
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| In: |
The lancet. Digital health
Year: 2025, Volume: 7, Issue: 10, Pages: 1-7 |
| ISSN: | 2589-7500 |
| DOI: | 10.1016/j.landig.2025.100933 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.landig.2025.100933 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S2589750025001153 
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| Author Notes: | Lawrence A Shaktah, Zunamys I Carrero, Katherine Jane Hewitt, Marco Gustav, Matthew Cecchini, Sebastian Foersch, Sabina Berezowska, Jakob Nikolas Kather |
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| 520 | |a Artificial intelligence (AI) is on the verge of reshaping cancer diagnostics through integration into digital pathology workflows. Despite the progression of AI towards real-world deployment, challenges in interpretability, validation, and clinical integration persist. AI models support the interpretation of stains including haematoxylin and eosin, enabling tumour classification, grading, and biomarker quantification, with clinical applications for targets such as HER2 and PD-L1. In addition, AI models enable the quantification of subtle microscopic patterns with prognostic and predictive values across tumour types. Herein, we provide an overview of the applications of AI in pathology and address emerging regulatory and ethical considerations. We also discuss the disparities in adoption across care settings and emphasise the importance of validation, human oversight, and post-deployment monitoring for the responsible implementation of AI in pathology-driven workflows. Furthermore, we highlight the technical advancements driving these developments, particularly the transition from hand-crafted machine learning workflows to deep learning, self-supervised learning for foundation models, multimodal models, and agentic AI. | ||
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