The current landscape of artificial intelligence in computational histopathology for cancer diagnosis
Abstract Artificial 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, un...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02212-z |
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| author | Aaditya Tiwari Aruni Ghose Maryam Hasanova Sara Socorro Faria Srishti Mohapatra Sola Adeleke Stergios Boussios |
| author_facet | Aaditya Tiwari Aruni Ghose Maryam Hasanova Sara Socorro Faria Srishti Mohapatra Sola Adeleke Stergios Boussios |
| author_sort | Aaditya Tiwari |
| collection | DOAJ |
| description | Abstract Artificial 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. |
| format | Article |
| id | doaj-art-964e85cecf054d82a9759774b64d458e |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-964e85cecf054d82a9759774b64d458e2025-08-20T03:07:43ZengSpringerDiscover Oncology2730-60112025-04-0116112510.1007/s12672-025-02212-zThe current landscape of artificial intelligence in computational histopathology for cancer diagnosisAaditya Tiwari0Aruni Ghose1Maryam Hasanova2Sara Socorro Faria3Srishti Mohapatra4Sola Adeleke5Stergios Boussios6Barts and the London School of Medicine and Dentistry, Queen Mary University of LondonBarts Cancer Institute, Cancer Research UK City of London Centre, Queen Mary University of LondonOncoFlowTMLaboratory of Immunology and Inflammation, Department of Cell Biology, University of BrasiliaGeneral Internal Medicine Doctorate Programme, University of HertfordshireSchool of Biomedical Engineering & Imaging Sciences, King’s College LondonDepartment of Medical Oncology, Medway NHS Foundation TrustAbstract Artificial 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.https://doi.org/10.1007/s12672-025-02212-zArtificial IntelligenceMachine LearningDeep LearningCancerHistopathologyComputational |
| spellingShingle | Aaditya Tiwari Aruni Ghose Maryam Hasanova Sara Socorro Faria Srishti Mohapatra Sola Adeleke Stergios Boussios The current landscape of artificial intelligence in computational histopathology for cancer diagnosis Discover Oncology Artificial Intelligence Machine Learning Deep Learning Cancer Histopathology Computational |
| title | The current landscape of artificial intelligence in computational histopathology for cancer diagnosis |
| title_full | The current landscape of artificial intelligence in computational histopathology for cancer diagnosis |
| title_fullStr | The current landscape of artificial intelligence in computational histopathology for cancer diagnosis |
| title_full_unstemmed | The current landscape of artificial intelligence in computational histopathology for cancer diagnosis |
| title_short | The current landscape of artificial intelligence in computational histopathology for cancer diagnosis |
| title_sort | current landscape of artificial intelligence in computational histopathology for cancer diagnosis |
| topic | Artificial Intelligence Machine Learning Deep Learning Cancer Histopathology Computational |
| url | https://doi.org/10.1007/s12672-025-02212-z |
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