AI-Driven Multiomics: A Vision for Personalised Medicine
This article from the PHG Foundation explores the transformative potential of AI-driven multiomics in personalised healthcare. Multiomics involves the combined analysis of diverse biological data layers, including genomics, transcriptomics, proteomics, and metabolomics, facilitated by artificial intelligence. This approach offers powerful insights for disease risk prediction, subtyping, and treatment response, surpassing the capabilities of single-dataset analyses. However, current applications remain largely confined to fragmented research settings. The health ecosystem currently lacks the infrastructure to manage the immense data volumes, computational demands, and specialized skills required for widespread implementation. Significant technical, ethical, and legal hurdles also pose challenges to integration. The authors argue that immediate planning is essential to ensure AI-driven solutions address real-world clinical needs and are equitably implemented. By convening expert stakeholders, the foundation aims to initiate discussions on overcoming these barriers. The ultimate goal is to prepare health systems for the scalable translation of these technologies, ensuring that the benefits of digital health transformation are accessible across generations and socioeconomic groups, rather than leaving vulnerable populations behind.
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AI-Driven Multiomics: A Vision for Personalised Medicine
This article from the PHG Foundation explores the transformative potential of AI-driven multiomics in personalised healthcare. Multiomics involves the combined analysis of diverse biological data layers, including genomics, transcriptomics, proteomics, and metabolomics, facilitated by artificial intelligence. This approach offers powerful insights for disease risk prediction, subtyping, and treatment response, surpassing the capabilities of single-dataset analyses. However, current applications remain largely confined to fragmented research settings. The health ecosystem currently lacks the infrastructure to manage the immense data volumes, computational demands, and specialized skills required for widespread implementation. Significant technical, ethical, and legal hurdles also pose challenges to integration. The authors argue that immediate planning is essential to ensure AI-driven solutions address real-world clinical needs and are equitably implemented. By convening expert stakeholders, the foundation aims to initiate discussions on overcoming these barriers. The ultimate goal is to prepare health systems for the scalable translation of these technologies, ensuring that the benefits of digital health transformation are accessible across generations and socioeconomic groups, rather than leaving vulnerable populations behind.
PHG Foundation