ARTIFICIAL INTELLIGENCE–ENABLED COMPUTATIONAL PATHOLOGY: FOUNDATION MODELS FOR NEXT-GENERATION PRECISION CANCER DIAGNOSTICS

Abstract

Computational pathology has emerged as one of the most transformative applications of artificial intelligence (AI) in modern oncology by enabling automated interpretation of whole-slide images, quantitative characterization of tumor biology, and integration of histopathological information with multimodal biomedical data for precision cancer diagnostics. Conventional pathology relies heavily on expert visual interpretation, which, despite its indispensable role in cancer diagnosis, is susceptible to interobserver variability, increasing workload, and limitations in detecting subtle morphological patterns associated with molecular alterations and clinical outcomes. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized representation learning across digital pathology, radiological imaging, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support precision diagnosis, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen computational pathology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-enabled computational pathology, emphasizing foundation models as transformative technologies for next-generation precision cancer diagnostics.