Social Acceptance of Artificial Intelligence in Biobanking

Abstract

The gradual implementation of artificial intelligence is changing healthcare practice. With recent progress in digitized data acquisition and computing infrastructure within healthcare systems, AI applications are now expanding into areas that were previously thought to be the exclusive domain of human experts. One such area is biobanking, at the intersection of translational medicine and datafication of research and ultimately clinical care. Biobanking 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 accommodate high-throughput sample-, image-, and data-handling operational models. However, the technological progress and gradual functional acceptance of AI also reveal the urgent need for further work on the social acceptance of such technologies. This work will investigate the latter aspects, and how the trust demonstrated by sample donors and researchers within biobanking operations might be able to extend to the utilization of AI tools. In particular, the manuscript highlights (i) how trust is a prerequisite for AI implementation (within biobanking) and (ii) to what extent factors such as the participants’ or researchers’ desire for control of samples and data, and the explainability and interpretability of AI, might influence future AI acceptance models.

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