This review highlights the transformative impact of digital pathology (DP) and artificial intelligence (AI) on cancer diagnosis and treatment. DP enables pathologists to access, analyze, and share high-resolution images, improving diagnostic accuracy and facilitating remote collaboration. AI enhances cancer diagnosis by automating tasks and analyzing the tumor microenvironment (TME), leading to the discovery of novel biomarkers. The Immunoscore (IS), an AI-assisted immune assay, shows strong potential in improving cancer diagnosis, prognosis, and treatment selection, surpassing traditional staging systems. Integrating DP and AI, particularly IS, into clinical practice can enhance personalized cancer therapy. The research underscores the importance of incorporating AI-driven technologies to improve cancer patient care and outcomes. The review explores the potential of DP in cancer management, influencing the clinical community toward more effective diagnostic and therapeutic strategies. DP allows for quantitative analysis of whole-slide images (WSI), enabling precise identification of histological patterns and morphological features. AI-based DP improves diagnostic accuracy and workflow efficiency by automating tasks and providing objective data. IS, a DP-immune assay, quantifies CD8+ and CD3+ T lymphocytes in the TME, offering a powerful prognostic tool. IS has been validated in various cancer types, demonstrating its robustness and consistency compared to expert pathologists' visual assessments. IS is a reliable and consistent assay that surpasses pathologists' visual evaluations, providing objective data for clinical decision-making. The integration of AI and DP into clinical practice is essential for improving diagnostic accuracy and enabling personalized treatment decisions. Challenges in implementing DP and AI include costs, regulatory approvals, data quality, and reimbursement. Despite these challenges, the adoption of DP and AI in clinical practice holds significant potential for improving diagnostic accuracy and efficiency. The review emphasizes the importance of standardizing IS and integrating it into clinical practice to enhance cancer diagnosis and treatment. The implementation of IS in a digital workflow can vary depending on the system setup and preferences of the pathology department. IS provides objective data to support clinical decision-making, enhancing the accuracy and consistency of cancer diagnosis and prognosis. The integration of AI and DP into clinical practice is crucial for advancing cancer care and improving patient outcomes.This review highlights the transformative impact of digital pathology (DP) and artificial intelligence (AI) on cancer diagnosis and treatment. DP enables pathologists to access, analyze, and share high-resolution images, improving diagnostic accuracy and facilitating remote collaboration. AI enhances cancer diagnosis by automating tasks and analyzing the tumor microenvironment (TME), leading to the discovery of novel biomarkers. The Immunoscore (IS), an AI-assisted immune assay, shows strong potential in improving cancer diagnosis, prognosis, and treatment selection, surpassing traditional staging systems. Integrating DP and AI, particularly IS, into clinical practice can enhance personalized cancer therapy. The research underscores the importance of incorporating AI-driven technologies to improve cancer patient care and outcomes. The review explores the potential of DP in cancer management, influencing the clinical community toward more effective diagnostic and therapeutic strategies. DP allows for quantitative analysis of whole-slide images (WSI), enabling precise identification of histological patterns and morphological features. AI-based DP improves diagnostic accuracy and workflow efficiency by automating tasks and providing objective data. IS, a DP-immune assay, quantifies CD8+ and CD3+ T lymphocytes in the TME, offering a powerful prognostic tool. IS has been validated in various cancer types, demonstrating its robustness and consistency compared to expert pathologists' visual assessments. IS is a reliable and consistent assay that surpasses pathologists' visual evaluations, providing objective data for clinical decision-making. The integration of AI and DP into clinical practice is essential for improving diagnostic accuracy and enabling personalized treatment decisions. Challenges in implementing DP and AI include costs, regulatory approvals, data quality, and reimbursement. Despite these challenges, the adoption of DP and AI in clinical practice holds significant potential for improving diagnostic accuracy and efficiency. The review emphasizes the importance of standardizing IS and integrating it into clinical practice to enhance cancer diagnosis and treatment. The implementation of IS in a digital workflow can vary depending on the system setup and preferences of the pathology department. IS provides objective data to support clinical decision-making, enhancing the accuracy and consistency of cancer diagnosis and prognosis. The integration of AI and DP into clinical practice is crucial for advancing cancer care and improving patient outcomes.