Background:The integration of artificial intelligence (AI) into early cancer detection marks a transformative shift in healthcare, offering powerful tools for the systematic analysis of complex, multimodal data. Precision oncology, in particular, stands to benefit greatly from AI applications across genomics, medical imaging, clinical decision-making, drug delivery, and therapeutic development. These innovations are reshaping cancer care by improving diagnostic accuracy, personalising treatment strategies, and enabling earlier detection through emerging technologies such as wearable sensor devices and mobile health applications.Goal:This article collection invites high-quality research that explores the pivotal role of AI in cancer diagnosis and personalised treatment. We welcome contributions that deepen our understanding of how AI can enhance clinical decision-making, improve patient outcomes, and shape the future of precision oncology. Suggested AI applications may span various types of oncology data, including electronic health records, medical imaging (radiology and digital pathology), molecular biomarkers (genomics, proteomics, metabolomics), and data from digital health sensor technologies.We encourage authors to submit original research articles, reviews, and innovative technical papers in areas including, but not limited to: • Advanced machine learning techniques in cancer diagnostics, including supervised, unsupervised, deep learning, and federated learning approaches. • AI-driven methods for data acquisition and preprocessing across diverse medical data types, such as imaging, genomics, and clinical records. • Feature extraction and selection techniques are designed for cancer-related datasets. • AI model training methodologies, evaluation metrics, and performance benchmarking. • Applications of large language models and natural language processing to analyse electronic health records, supporting early and accurate cancer detection and improved care planning. • AI applications across various cancer types, addressing challenges such as limited datasets, model interpretability, integration of multi-omics data, and ethical considerations. • Explainable AI (XAI) in cancer diagnosis, providing transparency and clinical interpretability through decision visualisation and feature importance analysis. • Innovative AI solutions in next-generation sequencing, radiological imaging, drug discovery, and drug delivery, accelerating advancements in cancer diagnosis, treatment, and therapeutic development.
Background:The integration of artificial intelligence (AI) into early cancer detection marks a transformative shift in healthcare, offering powerful tools for the systematic analysis of complex, multimodal data. Precision oncology, in particular, stands to benefit greatly from AI applications across genomics, medical imaging, clinical decision-making, drug delivery, and therapeutic development. These innovations are reshaping cancer care by improving diagnostic accuracy, personalising treatment strategies, and enabling earlier detection through emerging technologies such as wearable sensor devices and mobile health applications.Goal:This article collection invites high-quality research that explores the pivotal role of AI in cancer diagnosis and personalised treatment. We welcome contributions that deepen our understanding of how AI can enhance clinical decision-making, improve patient outcomes, and shape the future of precision oncology. Suggested AI applications may span various types of oncology data, including electronic health records, medical imaging (radiology and digital pathology), molecular biomarkers (genomics, proteomics, metabolomics), and data from digital health sensor technologies.We encourage authors to submit original research articles, reviews, and innovative technical papers in areas including, but not limited to: • Advanced machine learning techniques in cancer diagnostics, including supervised, unsupervised, deep learning, and federated learning approaches. • AI-driven methods for data acquisition and preprocessing across diverse medical data types, such as imaging, genomics, and clinical records. • Feature extraction and selection techniques are designed for cancer-related datasets. • AI model training methodologies, evaluation metrics, and performance benchmarking. • Applications of large language models and natural language processing to analyse electronic health records, supporting early and accurate cancer detection and improved care planning. • AI applications across various cancer types, addressing challenges such as limited datasets, model interpretability, integration of multi-omics data, and ethical considerations. • Explainable AI (XAI) in cancer diagnosis, providing transparency and clinical interpretability through decision visualisation and feature importance analysis. • Innovative AI solutions in next-generation sequencing, radiological imaging, drug discovery, and drug delivery, accelerating advancements in cancer diagnosis, treatment, and therapeutic development.