AI-Driven Environmental Precision Oncology: Integrating Big Data, Multi-Omics, Medical Imaging, and Exposomic Intelligence for Personalized Cancer Care.
DOI:
https://doi.org/10.22399/ijcesen.4533Keywords:
Artificial intelligence, Environmental science, Exposomics, Big data analytics, Precision oncology, Multi-omics integrationAbstract
Cancer is a complicated, multi-factorial disease, involving genetics, molecules, clinical factors, lifestyle, and environment. Precision oncology has advanced with genomics-based classification and AI-assisted diagnosis, but most existing models of personalized treatment are predominantly and usually only biologically driven and do not account for environmental conditions such as air pollution, toxic chemicals, climate stress, workplace, and social ecologies etc. Evidence published by environmental health and cancer epidemiology research shows that these exposures affect the development of cancer, its progression, the response to treatment, and survival. Combining big data analytics, artificial intelligence, multi-omics, advanced imaging, and environmental informatics offers an opportunity to create precision oncology, considering the environmental context. This study provides an AI-powered big data framework, aggregating the data collected from electronic health records, multi-omics data such as genomics, transcriptomics, proteomics, metabolomics, AI-improved imaging, and exposomics data of monitoring systems and geographic data. Machine-learning, deep-learning, predictive modeling, and explainable AI-based approaches are adopted to explain complex associations of genes with the environment, enhance the early detection of cancer, refine the risk assessment process, and customize treatments. By considering the latest publications, this paper presents the state-of-the-art AI-driven precision oncology, environmental health analytics, and exposomics, as well as some technical and ethical concerns, while laying out a potential scalable architecture for environmentally-aware personalized cancer care. The findings show that inclusion of environmental exposure information in AI-enabled oncology workflow leads to increased diagnostic accuracy, therapeutic uniqueness, and health equity in addition to promoting sustainable and preventive strategies against cancer. This work is a step forward in research in environmental precision oncology, and can provide useful recommendations for clinicians, researchers, and policy makers.
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