Biobanks and Biobank-Based Artificial Intelligence (AI) Implementation Through an International Lens

Abstract

Artificial Intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be the exclusive domain of human experts. One such area is biobanking, which forms a natural extension for AI-driven activities, both because biobanking is a foundational activity for downstream precision medical research, as well as because biobanking can increasingly accommodate high-throughput sample-, image- and data-handling operational models. The increasing AI-driven appetite for higher volumes of data and images often necessitates the cross-border collaboration of biobanks; likely to develop to one of the major forces dictating the international biobanking collaboration in the near future. However there are significant challenges ahead at the same time: firstly key practical issues surrounding the implementation of AI into existing research and clinical workflows, including data standardization, data sharing and interoperability across multiple platforms. Secondly governance concerns such as privacy, transparency of algorithms, data interpretation and how the latter might impact patient safety. Here a high-level summary of these opportunities and challenges will be presented from the perspective of AI driving more and stronger international collaborations in biobanking.