Keywords: Artificial intelligence; Histopathology; Deep learning; Cancer detection; Whole-slide imaging.
Introduction:
Between 2015 and 2025, artificial intelligence (AI) has significantly advanced the field of histopathology, addressing long-standing challenges in cancer diagnostics. Histopathology, the microscopic analysis of tissue architecture and cellular morphology, remains central to cancer diagnosis, grading, and prognostication. Yet, conventional workflows suffer from inter-observer variability, labor-intensive manual processes, and a global shortage of trained pathologists. The increasing complexity of precision medicine has intensified the need for accurate, efficient, and reproducible diagnostic tools.The rise of whole-slide imaging (WSI) has enabled computational pathology, allowing AI to analyze vast amounts of image data and extract features linked to clinical outcomes. Early studies relied on handcrafted features, but the field has shifted toward end-to-end deep learning, especially convolutional neural networks (CNNs), which effectively detect complex histological patterns. Subsequent models—such as ResNet, attention mechanisms, and vision transformers—have improved the analysis of spatial and contextual information in tissue samples.AI systems now support a wide range of tasks, including cancer detection, tumor classification, grading, prognosis, and even prediction of molecular markers like microsatellite instability. AI also enhances lab efficiency through tissue segmentation, stain normalization, and quality control.Progress has been accelerated by publicly available datasets such as Camelyon16, Camelyon17, PCam, BreakHis, and TCGA, which support model training and benchmarking. AI models have achieved expert-level performance in specific tasks, but generalizability across institutions and populations remains a challenge, alongside concerns about bias, interpretability, and regulation.Recent trends emphasize scalable, multimodal, and privacy-preserving approaches. Foundation models trained on millions of histology tiles now support diverse applications, while multimodal AI integrates pathology with genomic and clinical data. Federated learning enables secure, collaborative model development. These innovations mark a shift toward clinically integrated, explainable, and ethically aligned AI tools for diagnostic decision-making.
References
- Breen, J., Allen, K., Zucker, K., Adusumilli, P., Scarsbrook, A., Hall, G., Orsi, N. M., & Ravikumar, N. (2023). Artificial intelligence in ovarian cancer histopathology: A systematic review. Journal of Pathology Informatics, 14(1).
- Clinical Proteomic Tumor Analysis Consortium. (2023). Deep learning integrates histopathology and proteogenomics at a pan-cancer level. Cell Reports Medicine, 4(9).
- Doğan, R. S., & Yılmaz, B. (2023). Histopathology image classification: Highlighting the gap between manual analysis and AI automation. Frontiers in Oncology, 13.
- Slabaugh, G., Beltran, L., Rizvi, H., & Marouli, E. (2023). Applications of machine and deep learning to thyroid cytology and histopathology: A review. Frontiers in Oncology, 13,
- Tiwari, A., Ghose, A., Hasanova, M., Faria, S. S., Mohapatra, S., Adeleke, S., & Boussios, S. (2025). The current landscape of artificial intelligence in computational histopathology for cancer diagnosis. Discover Oncology.
- Wei Tan, J., & Jeong, W.-K. (2023). Histopathology image classification using deep manifold contrastive learning. In Proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2023).
Biography:
Nada Shamina is a young enthusiastic doctor with great interest in histopathology, currently she is working as a speciality registrar in chemical pathologist in the UK, graduated 2016 from university of Medical Sciences and Technology, Khartoum Sudan.
#UCJournals #ArtificialIntelligence #AI #Histopathology #DigitalPathology #ComputationalPathology #CancerDiagnosis #PrecisionMedicine #DeepLearning #MachineLearning #ConvolutionalNeuralNetworks #CNN #PathologyAI #MedicalAI #PathologyResearch #TumorDetection #CancerResearch #MedicalImaging #WholeSlideImaging #WSI #PathologyInnovation #DigitalHealth #AIinHealthcare #MedicalTechnology #PathologyEducation #BiomedicalAI #AIApplications #PathologyTools #PathologyWorkflow #PathologyAnalytics #PathologyData #MedicalResearch #OncologyAI #ClinicalPathology #Histology #TissueAnalysis #MedicalDiagnostics #PathologyCommunity #PathologyScience #AIinMedicine #HealthcareInnovation #SmartDiagnostics #AIPathologist #AIResearch #MedicalAIModels #PathologyTrends #HistopathologyAI #AIinOncology #MedicalDeepLearning #HealthcareAI #AIForCancer
