Abstract: The role of biobanking is undergoing a radical shift as it converges with artificial intelligence. Traditionally seen as static repositories, biobanks are now evolving into “intelligent infrastructures” that do more than just store samples; they actively drive discovery. This transformation is two-fold: high-quality, well-annotated biospecimens provide the essential foundation for training robust AI models, while AI-driven tools are simultaneously optimizing biobank operations. From using non-generative AI to predict sample degradation and assess tissue integrity to employing Large Language Models (LLMs) for automated metadata extraction, the integration of these technologies is making biobanking more efficient and scientifically potent than ever before.
Looking toward the immediate future, the emergence of multi-agent AI frameworks is set to orchestrate entire “end-to-end” processes within biobanks, from initial biospecimen access to final clinical application. These distributed AI systems can handle complex workflows, including image-based quality control and the creation of privacy-preserving synthetic datasets that allow for secure data sharing across borders. While significant challenges regarding data ethics, governance, and interoperability remain, the trajectory is clear. Biobanks are transitioning from simple warehouses of biological material into adaptive, digital engines that sit at the very heart of precision medicine and translational research.
Read more: https://journals.sagepub.com/doi/10.1177/19475535261445907