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We present a proof-of-concept for the extension of the Brain Imaging Data Structure (BIDS) to accommodate Computed Tomography (CT) data. With the growing volume of CT imaging across various medical fields, including neuroradiology and thoracic imaging, the need for data standardization is increasingly critical, especially in the context of artificial intelligence (AI) tools for medicine. This study demonstrates the conversion of OASIS-3 and National Lung Screening Trial (NLST) datasets into BIDS format and the development of a BIDS App for lung cancer risk prediction using the Sybil AI tool. The resulting framework promotes interoperable, accessible, and reusable data, fostering Open Science and enabling independent validation of AI models across diverse systems and datasets, ultimately addressing challenges like bias and overfitting in clinical settings.Clinical relevanceThis study enables the sharing and reuse of CT data within the research community, enhancing knowledge extraction and accelerating the development and validation of AI tools that can improve diagnostic accuracy and clinical decision-making across various medical fields.