Advancing Biobanking Through Artificial Intelligence and Emerging Technologies

The rapid evolution of artificial intelligence (AI) and related digital technologies is transforming the landscape and impact of biomedical research at an unprecedented pace. Biobanking, recognized as a foundational infrastructure for scientific discovery and application, now sits at the convergence of data-driven innovation and biospecimen stewardship. The articles in this special issue of Biopreservation and Biobanking collectively illustrate how AI, blockchain, and integrated digital systems can not only enhance traditional biobanking operations but also redefine their role in a global, data-centric research ecosystem. However, potential pitfalls in integrating these technologies must be addressed to keep pace with their rapid adoption.

A central theme emerging across these articles is the bidirectional relationship between AI and biobanking. As described by Tran, Rashidi, and Dhir in “Biobanking in the Era of AI: Convergence, Challenges, and Opportunities,” high-quality, robust data from well-annotated biospecimens remain essential for training robust machine learning models, while AI models are increasingly being deployed to optimize nearly every aspect of biobank operations. ¹ This adoption signals a transition from static repositories to dynamic, intelligent infrastructures capable of actively driving discovery.

At the same time, the ethical implications of this transformation remain front and center. The Science Policy Forum article contribution “With Power Comes Great Responsibility,” underscores the importance of addressing issues such as data privacy, algorithmic bias, and the need for adaptive governance as AI becomes more deeply embedded in biobanking workflows. ²

These considerations are further expanded in “Transforming Biobanking with AI: Perspectives from Leading Experts” by Abdelhafiz et al., which brings together international viewpoints to highlight both the opportunities and the global challenges associated with integrating AI into biobanking systems. ³ Together, these perspectives emphasize that the introduction of technological advancement must be accompanied by robust ethical frameworks and inclusive governance models.

Several studies in this issue demonstrate the practical implementation of AI in biobanking operations. The BEACON platform, presented by Ahmadi et al. in “BEACON: An AI-Powered Optimized Biobank Sample Management System Leveraging Real-World Data,” exemplifies how AI can be integrated to improve sample management, allocation, discoverability, and decision-making. ⁴ By integrating real-world data with advanced AI techniques, BEACON highlights the potential for intelligent systems to enhance efficiency while maintaining transparency and accountability.

Complementing these operational innovations, multiple contributions address the digitization and automation of what have been traditionally manual processes. Lee et al., in “Development of OCR-based Quality Control Process of Paper-based Consent Forms,” demonstrate how optical character recognition (OCR) technologies can streamline consent validation and reduce administrative burden, supporting the transition from paper-based to digital systems. ⁵ This innovation will further facilitate the transition from analog to digital systems while maintaining compliance with ethical, regulatory, and legal requirements while maintaining donor trust.

Similarly, Gramatiuk et al. in “Artificial Intelligence-Based Quality Control of Cell Lines” showcase how image-based AI models can improve the accuracy and reproducibility of cell line characterization, offering a scalable alternative to labor-intensive quality control methods. ⁶ These approaches reduce variability and resource demands.

AI is also beginning to reshape how biobanks engage with donor participants. The case study by van der Graaf et al., “Testing of an AI Chatbot Designed for Guiding and Assisting Participants at a Biobank Research Site,” highlights the potential of conversational agentic AI to enhance participant experience, improve accessibility, and streamline interactions at research biobank sites. ⁷ The iterative creation of such tools represents an important step toward more donor participant-centered biobanking models, where communication and engagement are facilitated through intuitive digital interfaces.

Another major theme across this issue is the emergence of decentralized biobanking. Barnes et al., in “A Beginner’s Guide to Decentralized Biobanking,” provide a conceptual framework for understanding how distributed technologies can address longstanding challenges related to data silos, transparency, provenance, and participant trust. ⁸ Building on a similar structure, Dewan et al. present an operational perspective in “Decentralized Biobanking to Empower Patient Engagement in Biospecimen Research: Operational Feasibility Case Study,” demonstrating how decentralized approaches can enhance donor participant engagement and potentially improve recruitment and retention. ⁹ These studies collectively suggest that decentralization may play a critical role in shifting biobanking toward more inclusive and participatory models.

The importance of integration and interoperability is further emphasized in “Transforming Biospecimen Management: A Roadmap for Integrated Sample Traceability in the Era of Global Research” by Sion et al. ¹⁰ This work highlights persistent challenges related to fragmented systems and limited traceability, proposing a roadmap for integrating multisystemic technologies such as AI, digital tracking systems, and interoperable data platforms. Such integration is essential for enabling seamless data sharing, improving reproducibility, and maximizing the scientific value of biobanked specimens in an increasingly global research landscape.

Looking ahead, the convergence of AI and biobanking presents an opportunity to fundamentally reimagine the role of biorepositories. Rather than serving solely as hidden infrastructure for research such as processing, storage, and distribution facilities, future biobanks are poised to become highly visible, intelligent, active platforms that integrate biospecimens with rich, multidimensional data. Advances in generative AI, natural language processing, and multiagent systems further expand the potential for automating complex processes, enhancing data accessibility, and speeding discovery while enabling new forms of scientific inquiry—unlocking the full potential of biorepositories.

However, these advancements also bring significant challenges. Ethical considerations, regulatory complexities, and issues of standardization and interoperability remain critical barriers to integration and thus widespread adoption. As highlighted across multiple contributions in this issue, addressing these challenges will require a coordinated effort to establish globally—adopted standards; promote interoperability, data management, and information access; and develop governance frameworks that are adaptable to rapidly evolving technologies. Importantly, the integration of ethical considerations into the design and deployment of AI and other advanced technology systems must be prioritized to ensure that technological advancements do not outpace societal trust and acceptance.

In conclusion, this special issue of Biopreservation and Biobanking highlights a transformative moment in the evolution of biobanking. The integration of AI and emerging technologies is driving innovation across operational, scientific, and ethical dimensions. While significant challenges remain, the collective insights from these studies provide a roadmap for harnessing these technologies to build more efficient, transparent, and participant-centered biobanking systems for human health. As the field continues to evolve to include expansion across all life sciences, collaboration across disciplines and sectors will be essential to realize the full potential of emerging technology-enabled biobanking in advancing precision medicine and improving global health outcomes.

AI Disclosure

ChatGPT 4.0 was used for the initial summarization of the contributed articles.

Authors’ Contributions

M.K.H. original draft, editing, and special section associate editor. G.H.G.: Editing and special section associate editor.