Abstract :
The field of cancer diagnosis and prognosis is undergoing a revolution thanks to the combination of digital pathology and artificial intelligence (AI). Despite being the gold standard, traditional histological procedures are frequently constrained by subjectivity, inter-observer variability, and the increasing workload of pathologists. Whole-slide imaging (WSI) in digital pathology makes it possible to digitize histology slides, improving pathological data processing, sharing, and storage. These digital images represent a rich source for computational analysis when combined with AI, especially machine learning (ML) and deep learning (DL) algorithms, which help with accurate and repeatable interpretations. Recent developments have shown that AI models can accurately predict patient outcomes, identify cancers, categorize tumor subtypes, and evaluate histological grading. Furthermore, new morphological and molecular biomarkers are being found thanks to AI-enhanced digital pathology, which makes individualized treatment plans easier. Its prognostic potential is further increased by integration with multi-omics data, which makes it possible to forecast treatment outcomes and disease progression. Widespread clinical use is hampered by a number of issues, despite the promise: data consistency, algorithm validation, regulatory compliance, and ethical worries about algorithmic bias and patient data privacy. However, the combination of AI and digital pathology has enormous potential to improve workflow efficiency, increase diagnostic accuracy, and revolutionize oncology patient care. The technological developments, present uses, and potential future developments of digital pathology and artificial intelligence in cancer diagnosis and prognostication are thoroughly covered in this study, which also highlights significant discoveries and persistent difficulties in integrating these advancements into standard clinical practice.
Keyword :
Digital pathology, Artificial intelligence, Cancer diagnostics, Prognostication, Machine learning.