This review explores the application of deep learning (DL) in histopathology and genomics, two critical components of precision oncology. Histopathology, which involves the examination of tissue slides stained with hematoxylin and eosin (H&E), is essential for diagnosing and subtyping cancers. Genomic profiling, on the other hand, provides molecular information that can predict patient outcomes and drug responses. The advent of modern AI methods, particularly machine learning (ML) and DL, has revolutionized the extraction of actionable insights from these data, potentially enhancing traditional evaluation workflows. The review highlights current and emerging applications of DL in histopathology and genomics, including basic diagnostic tasks such as cancer detection, grading, and subtyping, as well as advanced prognostic tasks like predicting survival probability, identifying genetic alterations, and assessing treatment responses. Key advancements include the use of weakly supervised learning, which reduces the need for manual annotations, and the integration of multimodal data, combining histopathological images with genetic data to improve model performance. Despite the potential benefits, the review also discusses the limitations and challenges of DL models, such as biases and the need for large, diverse datasets. It emphasizes the importance of fair and diverse data acquisition strategies and the need for regulatory approval and infrastructure improvements to integrate DL into clinical practice. The authors suggest that while DL has the potential to significantly enhance precision oncology, further research and collaboration are necessary to address these challenges and fully realize its benefits.This review explores the application of deep learning (DL) in histopathology and genomics, two critical components of precision oncology. Histopathology, which involves the examination of tissue slides stained with hematoxylin and eosin (H&E), is essential for diagnosing and subtyping cancers. Genomic profiling, on the other hand, provides molecular information that can predict patient outcomes and drug responses. The advent of modern AI methods, particularly machine learning (ML) and DL, has revolutionized the extraction of actionable insights from these data, potentially enhancing traditional evaluation workflows. The review highlights current and emerging applications of DL in histopathology and genomics, including basic diagnostic tasks such as cancer detection, grading, and subtyping, as well as advanced prognostic tasks like predicting survival probability, identifying genetic alterations, and assessing treatment responses. Key advancements include the use of weakly supervised learning, which reduces the need for manual annotations, and the integration of multimodal data, combining histopathological images with genetic data to improve model performance. Despite the potential benefits, the review also discusses the limitations and challenges of DL models, such as biases and the need for large, diverse datasets. It emphasizes the importance of fair and diverse data acquisition strategies and the need for regulatory approval and infrastructure improvements to integrate DL into clinical practice. The authors suggest that while DL has the potential to significantly enhance precision oncology, further research and collaboration are necessary to address these challenges and fully realize its benefits.