The current landscape of artificial intelligence in computational histopathology for cancer diagnosis

Journal article

Tiwari, Aaditya, Ghose, Aruni, Hasanova, Maryam, Faria, Sara Socorro, Mohapatra, Srishti, Adeleke, Sola and Boussios, Stergios 2025. The current landscape of artificial intelligence in computational histopathology for cancer diagnosis. Discover oncology. 16 (1), p. 438. https://doi.org/10.1007/s12672-025-02212-z
AuthorsTiwari, Aaditya, Ghose, Aruni, Hasanova, Maryam, Faria, Sara Socorro, Mohapatra, Srishti, Adeleke, Sola and Boussios, Stergios
AbstractArtificial intelligence (AI) marks a frontier in histopathologic analysis shift towards the clinic, becoming a mainstream choice to interpret histological images. Surveying studies assessing AI applications in histopathology from 2013 to 2024, we review key methods (including supervised, unsupervised, weakly supervised and transfer learning) in deep learning-based pattern recognition in computational histopathology for diagnostic and prognostic purposes. Deep learning methods also showed utility in identifying a wide range of genetic mutations and standard pathology biomarkers from routine histology. This survey of 41 primary studies also encompasses key regions of AI applicability in histopathology in a multi-cancer review while marking prospects to introduce AI into the clinical setting with key examples including Swarm Learning and Data Fusion. [Abstract copyright: © 2025. The Author(s).]
KeywordsArtificial intelligence; Cancer; Machine learning; Deep learning; Computational; Histopathology
Year2025
JournalDiscover oncology
Journal citation16 (1), p. 438
ISSN2730-6011
Digital Object Identifier (DOI)https://doi.org/10.1007/s12672-025-02212-z
Official URLhttps://link.springer.com/article/10.1007/s12672-025-02212-z
Publication dates
Online01 Apr 2025
Publication process dates
Accepted24 Mar 2025
Deposited14 Apr 2025
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Open
Output statusPublished
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