Biobanking in the Era of Artificial Intelligence: Convergence,Challenges,and Opportunities

Abstract

Artificial intelligence (AI) is advancing rapidly, transforming biomedical research and health care through software applications ranging from diagnostics to drug discovery. Biobanking resides at a unique intersection of this technological transformation, serving both as a foundation for training new AI models and as a beneficiary of AI-driven optimization. High-quality, well-annotated biospecimens enable robust machine learning, while AI methods in turn support automation, quality control (QC), predictive analytics, and workflow efficiency for biobanking operations. Emerging applications include non-generative AI methods, which have been used to predict sample degradation, stratify populations, and assess tissue integrity. Generative AI and large language models expand these capabilities by enabling synthetic data generation, metadata extraction, natural language–based interaction with biobank systems for both operational needs and training. Furthermore, newer multiagent approaches now demonstrate how distributed AI frameworks can orchestrate end-to-end processes. Case examples highlight early successes in automated image-based QC, natural language processing for metadata extraction, and privacy-preserving synthetic datasets to enable secure data sharing. Looking ahead, AI promises to reshape biobanking as both an operational and scientific engine, with opportunities in business intelligence, workflow optimization, and personalized education. Challenges around data quality, interoperability, governance, and ethics remain, but the convergence of AI and biobanking points to a future where repositories evolve into intelligent, adaptive infrastructures that actively drive discovery, accelerate translational research, and advance precision medicine.