Harnessing Artificial Intelligence for Cancer Prevention, Early Diagnosis, and Personalized Treatment - Volume II

This Research Topic is the second volume of “Harnessing Artificial Intelligence for Cancer Prevention, Early Diagnosis, and Personalized Treatment”. It continues the work of Volume I and welcomes new contributions on the same questions.The field of oncology has witnessed remarkable strides in early detection and treatment, yet cancer remains a formidable threat with high mortality rates worldwide. Traditional methods, although effective to some extent, struggle to keep pace with the evolving nature of malignancies. Emerging technologies, particularly in Artificial Intelligence (AI), are poised to offer innovative solutions that surpass existing strategies. With machine learning (ML) and deep-learning (DL) methods, AI holds the potential to revolutionize the assessment of risk factors, enhance screening procedures, and develop personalized therapies. Numerous AI-assisted models have been developed for the screening and identification of cancer-specific compounds; however, there is still a significant research gap in employing AI to evaluate the etiology and risk factors for certain malignancies, which could improve clinical outcomes dramatically.Volume II aims to build on this foundation by exploring the transformative potential of AI in cancer care. It focuses on AI’s role in early detection, risk stratification, and personalized interventions, and on moving promising models toward clinical use. This includes investigating supervised and unsupervised models that address ethical challenges and improve AI model efficacy in clinical settings. The overarching objective is to augment traditional methods with AI’s capabilities to drive a deeper understanding of cancer dynamics and treatment modalities.To gather further insights within the boundaries of AI applications in cancer prevention and treatment, we welcome articles addressing, but not limited to, the following themes:· Development of AI models predicting individual cancer risk utilizing multi-omics datasets· AI-powered image analysis for early tumor detection· Creation of personalized preventive measures by integrating genetic, environmental, and demographic factors· Innovative advancements in cancer preventive measures· Enhanced precision in diagnosis and treatment through AI techniques· Artificial Intelligence’s role in predicting disease outcomes with personalized therapy approaches· New: Validation of AI models in independent clinical cohorts· New: Explainability, bias, and fairness of AI models in cancer careSpecific sub-thematic areas include:· Designing AI models with multi-omics datasets for cancer preventive measures· Assessing etiological factors through AI for early-stage malignancies· Clinical applications of AI models for personalized cancer therapy· New: Integration of imaging, pathology, and molecular data in multimodal AI modelsPlease note that manuscripts consisting solely of bioinformatics or computational analysis of public omics databases, without relevant functional validation (clinical cohort, biological validation in vitro, or in vivo), are out of scope for this Research Topic. Topic editors: Pradakshina Sharma, Maria Antonella Laginestra Source: https://www.frontiersin.org/research-topics/86877/harnessing-artificial-intelligence-for-cancer-prevention-early-diagnosis-and-personalized-treatment---volume-ii