Digital Biobanking in the Era of Artificial Intelligence

The terminology "Biobank" is used for the organized collection of biological materials consisting of tissue samples, blood, serum, body fluids, and DNA/RNA materials for scientific research. A biobank extracts the information from the biospecimens to generate databases, which are continuously updated. Over the years, there have been significant advances in digital pathology and artificial intelligence (AI). Digital pathology primarily relies on whole-slide imaging and cloud computing. AI, on the contrary, is able to handle vast amount of data and to recognize the patterns of the histopathology images in more meaningful ways. A digital biobank is defined as the integration of the bio-specimens with whole-slide images, molecular data, longitudinal clinical information, and metadata. Digital biobanking can do real-time monitoring of the quality of the biospecimen, rapid retrieval of data, and AI-based research work. It may take a pivotal role in precision medicine by utilizing AI-based models for patient management. This review discusses the evolution from conventional to digital biobanking and emphasizes AI-based quality control, multimodal data integration, and predictive analysis in oncology. In addition, virtual biobanks, hybrid physical-virtual models, and digital twins are also discussed. The article also discusses the key challenges related to the implementation of digital biobanking, including standardization, interoperability, and sustainability.