Imaging Biobanking: A Strategic Resource for AI-Enabled Biomedical Research and Precision Medicine

Abstract

In the era of precision medicine, the integration of artificial intelligence (AI) with large, multimodal biomedical datasets is reshaping healthcare research and clinical practice. The necessary infrastructure for this change is provided by imaging biobanks, which are organized collections of medical images connected to clinical, molecular, and demographic data. They curate a variety of imaging modalities, including MRI, CT, ultrasound, and PET, in standardized formats like DICOM, enhanced with harmonized metadata, in contrast to traditional biospecimen banks. This aids in the extraction of quantitative radiomic features and makes it easier to find imaging biomarkers that improve prognosis, diagnosis, and treatment choices. While radiogenomics efforts combine imaging with multi-omics data to enable tailored treatment strategies, global initiatives like The Cancer Imaging Archive and the Breast Imaging Data Commons show the viability and impact of these resources. However, the majority of existing AI models run the risk of bias and poor performance in genetically diverse populations like those in India because they were primarily trained on Western datasets. Developing indigenous imaging biobanks can mitigate these gaps and support AI tools tailored for local diagnostic and prognostic needs. Similar frameworks can also advance research in plant sciences, veterinary medicine, and materials engineering. Strong governance, Standardization, and Interoperability across institutions are critical for imaging biobanks to become successful in India . Federated learning offers a privacy-preserving method for collaborative AI development without centralised data transfer. For underrepresented regions, its necessary to adapt such \strategies to ensure equitable precision medicine from a scientific and ethical standpoint.. ```````````

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