Artificial intelligence (AI) is revolutionizing cancer research and clinical practice, with applications in early diagnosis, pathological imaging analysis, treatment decision-making, personalized therapy, drug development, and cancer management. By leveraging advanced techniques such as deep learning and machine learning, AI can integrate and analyze complex multimodal data, including imaging, genomics, transcriptomics, and proteomics, thereby extracting critical insights. This significantly improves the accuracy and efficiency of cancer diagnosis and optimizes treatment strategies. For example, in cancer biology research, AI excels at integrating diverse datasets and identifying patterns to uncover tumor heterogeneity and microenvironment characteristics while predicting responses to immunotherapies. In digital pathology and imaging analysis, deep learning models demonstrate remarkable capability in identifying complex image features and providing quantitative assessments. In drug discovery research, AI assists in identifying novel therapeutic candidates and repurposing existing drugs, substantially reducing development costs. Additionally, the automation capabilities of AI have proven highly effective in radiotherapy, alleviating the workload of medical professionals and improving the efficiency of treatment workflows. Despite these advancements, challenges remain in the translation of clinical practice, including limited model interpretability, lack of standardized technologies, and regulatory obstacles.
Looking ahead, AI holds significant promise with the integration of multimodal data, the development of personalized therapeutic approaches, and the refinement of decision-support systems. Enhancing the clinical validity and usability of AI models, establishing open data-sharing platforms, and implementing comprehensive regulatory frameworks are critical to facilitating its widespread application. In summary, AI is expected to bring breakthroughs to oncology, enabling personalized, precise, and intelligent solutions across the entire process of diagnosis and treatment, ultimately delivering more effective therapies to patients.