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.
Introduction
Artificial intelligence (AI) is advancing at an unprecedented pace, transforming medicine and biomedical research. 1 From diagnostic imaging and pathology to clinical decision support and drug discovery, the capabilities of modern AI systems are rapidly evolving from experimental prototypes to indispensable tools.2–4 The convergence of high-throughput computing, cloud infrastructure, powerful portable computers, and increasingly sophisticated machine learning (ML) algorithms has accelerated this trajectory, enabling analyses of vast volumes of complex and diverse data. At the same time, regulatory, ethical, and implementation frameworks are beginning to adapt, creating new opportunities for AI integration across the translational research spectrum. 5
Biobanking stands at a critical intersection of this technological revolution. As a field fundamentally rooted in the systematic collection, storage, and distribution of high-quality biological materials, biobanking is uniquely positioned to both support and benefit from AI—with well-annotated biospecimens serving as a foundation for training and validating robust AI models, particularly those aimed at precision medicine applications.6,7 Alternately, AI methods themselves can optimize nearly every aspect of biobank operations—from automated sample tracking and quality control (QC) to predictive analytics for resource allocation and demand forecasting.
The synergy between AI and biobanking extends beyond operations to scientific discovery. 6 AI-enabled multimodal integration of genomic, proteomic, clinical, and imaging data with banked specimens will reveal new biomarkers, disease subtypes, and therapeutic targets. Advanced natural language processing (NLP) and the use of large language models (LLMs), likewise, help unlock the vast quantities of unstructured metadata historically underutilized by biobanks, improving interoperability, facilitating large-scale research collaborations, optimizing workflows, and driving biobanking education.8,9 Thus, biobanking is not merely a passive infrastructure for AI, but an active and essential partner, and in some cases, a driver in its development, deployment, and education. This narrative review examines the current and emerging roles of AI in biobanking, with a focus on both operational and scientific applications.
Literature Review Methods
Literature review was conducted using PubMed and Google with “artificial intelligence,” “machine learning,” “biobanking,” “biobank,” “biospecimen storage,” and “biorepositories” as primary search parameters. Additional parameters such as “education,” “privacy,” “data access,” “data quality,” and “regulatory” were added for these targeted topics. These searches also helped identify and highlight case studies illustrating successes; challenges related to data quality, privacy, and data access; and outlining future directions where AI may further transform biobanking. Together, these perspectives underscore how the integration of AI and biobanking can create a mutually reinforcing ecosystem for accelerating biomedical research and enhancing global health outcomes.
Overview of AI Methodologies
AI is a field of computer science focusing on systems that can conduct complex tasks normally performed by humans.10,11 This includes reasoning, decision making, and generating new information. ML is a subset of AI where computer systems learn from new data and experiences to improve AI performance. In practice, AI encompasses a diverse set of computational approaches, many of which differ in how they learn from data and generate outputs. For those less familiar with the field, it is helpful to distinguish between several broad categories of AI methods that are increasingly relevant to biomedical and biobanking contexts. Figure 1 illustrates the various branches of AI, namely generative and non-generative AI, which are described in detail below. Readers who wish to learn more about these concepts can consult the open access special issue on AI published in Modern Pathology. 11
Types of artificial intelligence (AI) and biobank applications. The figure differentiates generative versus non-generative approaches, along with their subdivisions and potential applications in biobanking. Artificial intelligence can now be divided into non-generative versus generative AI. Often, non-generative AI is used for predictive analysis, which can be subdivided into supervised, unsupervised, and reinforcement learning applications. In contrast, large language models, image generation, and multiagentic frameworks fall under generative AI methods. The figure highlights AI applications for biobanking, differentiating current (green) uses versus future uses (red). The overlapping zones between current and future show applications that are entering current practice.
Generative versus non-generative AI
Traditional (“non-generative”) AI systems are designed primarily for classification, prediction, or decision making. 10 These systems produce discrete outputs (e.g., a label, a probability score) and are optimized for specific tasks. By contrast, generative AI models create new data resembling patterns found in their training sets.11,12 Generative AI has been employed using “chatbots” for conversational purposes and produces realistic images and videos.
Non-generative AI
Non-generative AI involves a range of ML methods that can play important roles in biobanking. These ML approaches could estimate sample viability based on historical data, while clustering algorithms can help stratify patient populations to enrich downstream studies. Each of these methods relies on high-quality, well-curated data—underscoring the vital role biobanks play not only as custodians of specimens but also as enablers of AI development. Examples of non-generative AI techniques include logistic regression, linear regression, decision trees, random forest, support vector machine, and convolutional neural networks (CNNs) (Fig. 2).11,13 These are described below:
Common non-generative machine learning approaches. Panel A visually shows linear versus logistic regression. Both are captured on an x–y plane. For logistic regression, the data show the predicted line (dotted line) produced by the regression model and compared against the true data relationship (solid line). The line is characterized by the equation of the line (y = mx + B), and the coefficient of determination (R2) describes how well the model describes the relationship. For logistic regression, the data are binary (e.g., disease [red] vs. no disease [blue]). As such, a logistic curve better fits this data and is described by the equation shown in the figure. Panel B compares decision trees versus random forest. These trees show how each iteration results in two or more possible decisions for a given dataset. For random forest, there are effectively multiple decision trees that help classify data. Panel C illustrates support vector machine (SVM), which produces a hyperplane (solid line) bounded by margins (dotted line). The goal of SVM is to find the best hyperplane to separate out-groups of data (e.g., disease [red] vs. no disease [blue]). Panel D shows a convolutional neural network starting with an image serving as the data input. The data enter a convolutional layer to extract features from the original image. Next, the pooling layer reduces the spatial size of the extracted feature maps and then flattens them into a single-dimensional vector (i.e., a series of numbers). Finally, the fully connected layer is where the predictions are made, showing neurons (circles) connected to each other and applying trainable weights and biases to make a correct decision.
Logistic regression
Logistic regression is a widely used statistical learning method for classification tasks, particularly in biomedical research. In the context of biobanking, logistic regression has been applied to risk prediction models for sample quality degradation, where predictors such as storage time, temperature fluctuations, and freeze–thaw cycles are used to estimate the likelihood of compromised biospecimens.14,15
Linear regression
Within biobanking, linear regression has been used to assess how pre-analytical variables (e.g., processing time, centrifugation speed, or aliquot volume) impact continuous quality metrics such as RNA integrity number or protein concentration.16–18 For example, researchers can model how increasing storage duration influences biomarker degradation rates. Its interpretability and simplicity make linear regression a common baseline tool in biobank operational studies and in evaluating long-term biospecimen stability.
Decision trees
Decision trees are non-parametric, tree-structured models that recursively partition the data space into homogeneous subsets based on splitting rules derived from input variables. Their hierarchical, rule-based structure makes them easy to visualize and interpret, enabling researchers to understand how pre-analytical and storage factors influence biospecimen quality. In biobanking practice, decision trees have been used to classify samples as “high quality” or “at risk” based on combinations of variables such as storage temperature, handling time, and donor characteristics. 19 This interpretability allows biobank managers to identify critical decision points in sample processing pipelines and implement corrective workflows accordingly.
Random forest
Random forests extend decision tree methodology by constructing an ensemble of multiple trees, each trained on a bootstrap sample of the data and a random subset of features. Predictions are aggregated across the ensemble, yielding robust and accurate models.19,20 In biobanking, random forests have proven useful for predicting complex outcomes such as sample usability for downstream omics analyses, based on diverse metadata fields ranging from donor demographics to sample handling parameters.
Support vector machine
Support vector machine (SVM) is a powerful supervised learning technique designed for classification and regression by finding an optimal hyperplane that maximizes the margin between classes. By employing a mathematical tool called a kernel function, SVMs can handle non-linear relationships, which are common in biological and operational data. In biobanking applications, SVMs have been used to classify tissue samples by molecular subtype based on high-dimensional omics data or to distinguish between high- and low-integrity samples using spectroscopic or image-derived features.21,22
Convolutional neural networks
CNNs are a class of deep learning models designed for structured data with spatial hierarchies, such as images 23 and spectroscopy. 24 In the biobanking domain, CNNs have been applied to automated histology and pathology image analysis, including quality assessment of tissue sections prior to storage. For example, CNN-based models can distinguish between necrotic and viable regions in tumor tissue samples, ensuring that only high-quality sections are stored for future research. 23 Additionally, CNNs have been used to analyze images from cryostorage monitoring systems, identifying subtle anomalies such as frost buildup or improper tube placements that may compromise long-term biospecimen integrity. 25
Generative AI
As discussed, generative AI is a domain where these techniques can now produce new, potentially original content such as text, images, music, video, and even software code. Modern chatbots not only employ LLMs but, in certain situations, also use multiple AI agents, each of which is specialized in a specific domain to improve performance. Among these developments, multiagent AI systems are emerging as particularly relevant to biobanking.
Large language models
Trained on vast volumes of text, LLMs can understand, summarize, and generate human-like language. Within biobanking, LLMs may assist in automatically extracting metadata from free-text clinical notes, harmonizing terminology across biorepositories, or even drafting standardized operating procedures. 26 Because these systems interface with researchers in natural language, LLMs also lower barriers to interacting with complex data systems, reduce language barriers for non-native speaking investigators, and perhaps facilitate broader engagement with biobank resources. 9
Multiagent systems
AI is no longer limited to single models. Increasingly, researchers are turning to multiagent systems, in which multiple AI “agents” interact to solve problems collaboratively.27,28 Each agent may specialize in a different task—for instance, one handling data ingestion, another QC, and another analyzing usage patterns. In a biobanking context, multiagent systems could coordinate end-to-end workflows: automatically receiving new samples, checking data integrity, updating laboratory information systems, and forecasting future demand. 29 Simple applications could involve using multiagent systems to write, check, and edit procedures and protocols for both investigators and biobanking operations.
AI performance metrics
As more AI solutions become available in the biobanking space, users need to understand how to evaluate the performance of these systems. The type of AI performance assessment depends fundamentally on whether the model is generative or non-generative. Both categories share certain common metrics—such as accuracy, precision, recall, and F1 score—but differ in the way performance is quantified and validated due to their underlying objectives.
Non-generative AI performance metrics
For non-generative AI, evaluation is typically rooted in statistical measures of discrimination, calibration, and generalizability. 30 Common metrics include accuracy for balanced datasets, clinical sensitivity and specificity for diagnostic models, and area under the receiver operating characteristic curve for overall discriminative power. 31 Calibration metrics such as the Brier score assess how well predicted probabilities align with observed outcomes. In regression contexts, measures like mean squared error, coefficient of determination (R2), and mean absolute error quantify predictive accuracy. 30 Cross-validation, hold-out testing, and external validation are used to test generalizability and guard against overfitting—critical steps in biomedical and operational AI applications.
Generative AI performance metrics
In contrast, generative AI models require multidimensional performance evaluation because they generate open-ended outputs.30,32 Objective metrics may include perplexity for LLMs, Fréchet inception distance or inception score for image generation, and Bilingual Evaluation Understudy, Recall-Oriented Understudy for Gisting Evaluation, or Metric for Evaluation of Translation with Explicit ORdering for text translation and summarization tasks. 33 However, these measures often fail to capture semantic quality, factual accuracy, or ethical soundness—areas where human-in-the-loop and multicriteria evaluations (e.g., expert review, preference modeling, or composite quality indices) are increasingly employed. For large-scale LLMs, alignment benchmarks such as Massive Multitask Language Understanding, Truthful Question Answering Benchmark, and Beyond the Imitation Game Benchmark are used to assess reasoning, factual grounding, and safety behaviors. 34
Overall, while non-generative AI performance is often characterized by quantitative precision and reproducibility, generative AI requires qualitative and context-aware assessments that combine automatic metrics with human judgment. The future of AI evaluation is moving toward hybrid frameworks integrating technical performance, human perception, ethical alignment, and task-specific utility—particularly as AI systems, including those that may appear in biobanks, transition from narrow applications to general, adaptive intelligence across domains such as medicine, engineering, and policy.
Real-World Applications
AI is already being integrated into biobanking across multiple stages of the specimen, data lifecycle, and complex workflows. In more advanced settings, ML models can help with QC or even extract/predict data from electronic health record (EHR) systems. Collectively, these applications position modern biobanks not only as repositories of specimens but also as foundational platforms for scalable, learning health systems for AI use, and development. We summarize some case examples below:
Automated imaging QC for biobanked images
QC is essential for biobanking operations regardless of whether the materials are data, liquid/solid tissue specimens, or glass slides.35,36 For liquid samples such as blood and its components, image-based QC could be employed to determine the presence of common analytical interferences such as hemolysis, icterus, and/or lipemia. In brief, hemolysis, icterus, and lipemia can impact tests that rely on photometric techniques. 37 Hemolysis is very common and may be caused by in vitro or in vivo mechanisms. Although spectrophotometric methods have been used to detect hemolysis, icterus, and lipemia, AI imaging techniques show promise in further automating the process and perhaps achieving higher accuracy than spectrophotometry. 38 The cost and ease of use for spectrophotometric versus AI-based imaging technologies have not been studied. However, both approaches are reagent free and can rely on existing optical methods employed in fully automated clinical analyzers. In terms of equipment, AI methods only require a camera, which may be cheaper than sophisticated spectrophotometric techniques. However, development and full clinical validation of AI-based methods for measuring hemolysis and other sample interferences—such as lipemia and icterus—would require manufacturers to conduct formal validation studies for Food and Drug Administration (FDA) submission, resulting in additional development and regulatory costs.
For solid tissue and slides QC, digital-pathology models trained on hematoxylin and eosin/immunohistochemistry slides have been shown to quantify tumor cellularity reliably, improving consistency in choosing tissue blocks for sequencing or other molecular tests—a critical biobanking step that maximizes nucleic acid yield/quality from limited specimens. 39 Demonstrations include deep learning and classical pipelines with strong agreement to pathologists, which can help reduce interobserver variability when using an AI-assisted workflow. Within the United Kingdom (UK) Biobank imaging projects, a statistical ML system was developed to flag abnormalities and data corruption automatically (“auto-QC”) by comparing each new scan to a learned normative model, accelerating high-volume QC and helping protect downstream analyses built on imaging-linked biospecimens. 40
NLP and LLMs for extracting and predicting features from EHRs
Wheater et al. 41 validated an NLP pipeline that mines radiology reports to derive brain imaging phenotypes at scale, enabling richer trait curation for biobank participants without manual abstraction. Specifically, the group used anonymized text brain imaging reports from a stroke and transient ischemic attack patient cohort obtained from a regional hospital to develop and test the NLP algorithm. Two experts marked up text in 1692 reports for 24 cerebrovascular and other neurological phenotypes for developing and testing a rule-based NLP algorithm first within the cohort study and then further evaluating the algorithm in reports from the regional hospital.
A recent analysis compared EHR-based risk models to polygenic scores across multiple biobanks, testing generalizability when models are trained in one biobank and evaluated in others. 42 This demonstrates how ML on routinely collected clinical variables linked to biospecimens can scale across repositories and populations. LLMs have been evaluated for extracting biological entity names (e.g., cell lines) from descriptions, showing that modern LLMs can structure noisy, free-text metadata—a common bottleneck in biobanking where accurate, standardized annotations drive findability and reuse. 43
Privacy-preserving synthetic cohorts to enable model development
Generative AI also offers a promising strategy to address privacy and data sharing challenges in biobanking by creating synthetic datasets that preserve the statistical properties of real biospecimen-associated data without exposing identifiable patient information.44,45 Techniques such as generative adversarial networks and variational autoencoders can be trained on genomic, imaging, or clinical metadata to produce synthetic samples that mirror real-world distributions while eliminating direct linkages to individual donors. 46 This approach enables broader data sharing across institutions and researchers, fostering collaborative discovery while reducing risks of reidentification and regulatory non-compliance. For example, synthetic multiomics datasets can be used to test and validate bioinformatics pipelines before applying them to protected biobank data, and simulated clinical metadata can support the development of ML models without requiring direct access to sensitive health records. 47 By balancing utility with privacy/confidentiality, synthetic data generation is a practical and ethically aligned solution to accelerate research within the biobanking ecosystem. Examples of applications have involved UK Biobank investigators generating privacy-preserving synthetic datasets of smokers and showed that prognostic models for lung cancer trained on synthetic data can perform competitively, supporting data sharing and external method development when direct access to identifiable data is restricted. 48 As an extension of this, UK Biobank has partnered with academic groups in recent years to build a “global health foundational model” aimed at learning from biobank data and generating high-fidelity synthetic health records for research while maintaining privacy—positioning generative AI as infrastructure for future biobank analytics and simulation. 49
Future Directions
The future of biobanking is poised to be increasingly shaped by AI. Advances in AI are expected to enable deeper integration of biospecimen data with multimodal, multiomic, clinical, and population-scale datasets, creating a foundation for more precise cohort selection, automated quality assurance, and predictive modeling of sample utility (Fig. 3). Potential opportunities in the coming years will include AI biobanking applications in business intelligence, automation, education, as well as integration with laboratory information management systems.
Opportunities for AI in the biobanking workflow. The figure shows the biobanking process, from left to right, where a patient of interest (red) is identified out of a heterogeneous population (blue and grey patients). The patient of interest consented, with data collected from the electronic health record (EHR), and blood samples collected. These samples are labeled and accessioned into the laboratory information management system (LIMS), evaluated by a quality control (QC) step, and stored in a freezer in this case. Samples are then analyzed for some purpose (i.e., next-generation sequencing [NGS], gas chromatography [GC]–mass spectrometry [MS]). These data are also added to the LIMS/data storage and output as results for users. The lower half of the figure shows how AI can be applied to each phase of this process.
Business intelligence and workflow optimization
AI and ML are increasingly being leveraged in biobanking to support not only scientific discovery but also business intelligence and operational decision making.8,50,51 Similarly, AI-driven optimization can streamline routine workflows, such as automating sample tracking and inventory management, 46 predicting maintenance, 52 and monitoring QC, thereby reducing labor-intensive manual tasks and minimizing human error. It must be noted that there is a clear distinction between automation and AI, where automation functions based on predefined rules for repetitive tasks (e.g., robotics for fluid handler systems), whereas AI involves systems controlled by software that can learn, adapt, and make intelligent decisions. 53 Although automation and AI can work together, automation does not require AI, and automated processes can remain efficient without AI.
AI/ML for business intelligence in biobanking
Business intelligence refers to the use of data-driven processes, analytical methods, and digital tools to transform raw operational information into actionable insights that guide strategic and operational decision making. 54 Predictive modeling can integrate scientific value with economic considerations to identify which biospecimens are most important to retain for future research. 55 For example, AI algorithms trained on historical request patterns can anticipate demand for certain disease cohorts or biospecimen types, enabling biobanks to align storage strategies with anticipated market and scientific needs. ML can also support cost–benefit analyses, balancing the high expense of long-term cryostorage with the projected utility of specimens for precision medicine, clinical trials, or industry partnerships. This predictive capacity transforms biobanks from passive repositories into dynamic resources that actively align their operations with both scientific priorities and financial sustainability.
AI/ML for operational efficiency in biobanking
Beyond business intelligence, AI/ML also offers significant opportunities to improve day-to-day biobank operations by reducing labor costs, streamlining workflows, and minimizing human error. For instance, inventory management systems enhanced with AI, including recurrent neural networks, can automatically flag low-volume aliquots, predict when (i.e., before it happens) freezer capacity will be exceeded, and suggest optimal storage layouts.52,56 Machine vision tools have been deployed for real-time QC, such as identifying labeling errors, monitoring frost buildup in cryostorage units, or detecting specimen quality anomalies prior to archiving. Furthermore, robotic aliquoting and retrieval systems, guided by AI scheduling algorithms, can automate repetitive tasks, significantly reducing manual labor requirements and turnaround times. 57 These operational efficiencies not only improve specimen quality and reduce waste but also enhance compliance with regulatory standards by ensuring more consistent and traceable processes. Together, such applications underscore the transformative role of AI/ML in making biobanking both more cost-effective and more scalable.
AI in biobanking education
AI is increasingly being integrated into biobanking education, creating new opportunities to advance both technical and conceptual training for laboratory professionals, researchers, and students. 50 One of the most transformative applications lies in the development of advanced learning resources. AI-driven tools such as intelligent tutoring systems and adaptive e-learning platforms can generate dynamic, interactive modules that adjust in complexity depending on the learner’s level of expertise.58,59 In parallel, NLP and LLMs can curate and summarize biobanking literature, produce explanatory visualizations, and even provide instant answers to learner queries. This ensures that trainees remain current with rapidly evolving standards, technologies, and regulatory frameworks.
Another critical area where AI supports education is in improving training on data analysis. Modern biobanking requires familiarity with large-scale datasets spanning genomics, proteomics, imaging, and clinical metadata.60–62 AI can simplify the complexity of these datasets by automating pattern recognition, highlighting QC concerns, and demonstrating how analytical pipelines work in practice. Educational dashboards powered by ML can allow trainees to experiment with simulated biobank data, test hypotheses, and receive immediate feedback on their analytical decisions. Such tools help bridge the gap between theoretical learning and practical competence in data-driven biobanking.
AI also enhances hands-on skills training through immersive technologies such as augmented reality, mixed reality, and virtual reality simulations. 61 By combining AI with virtual simulations, educators can create realistic laboratory environments where learners practice critical tasks such as biospecimen handling, freezer inventory management, and compliance documentation. These environments can automatically assess trainee performance, flag procedural errors, and recommend improvements. This approach reduces reliance on physical resources, minimizes biosafety risks during training, and enables scalable education across geographies.
Finally, AI enables personalized education by tailoring content delivery to individual learning styles, pace, and goals.63,64 Adaptive learning systems monitor performance, detect knowledge gaps, and suggest customized modules or microlearning exercises to reinforce weak areas. For biobanking professionals with diverse backgrounds—from pathology to informatics and regulatory affairs—personalized education ensures that training remains relevant, efficient, and aligned with professional trajectories. In the long run, AI-enabled personalization fosters a more skilled and confident workforce, better prepared to manage the complex interplay of biological materials, data, and technology that define modern biobanking.
Biobanking as a foundation for AI development
Biobanks are uniquely positioned to serve as foundational infrastructure for the development, validation, and long-term governance of AI in health care and biomedical research. 7 By systematically collecting biospecimens linked to longitudinal clinical, laboratory, imaging, and outcomes data, biobanks provide the high-quality, well-annotated datasets required to train and validate robust ML models. Unlike many ad hoc datasets assembled for single studies, biobank resources offer scale, diversity, and traceability—attributes that are essential for reducing bias, supporting generalizability, and meeting emerging regulatory expectations for AI transparency and performance evaluation.
Synthetic data
Beyond their role as primary training and validation datasets, biobanks also function as critical source material for the generation of synthetic data. 45 As privacy concerns, data access restrictions, and regulatory constraints increasingly limit the direct sharing of patient-level data, synthetic data approaches have gained traction as a means to support AI development while mitigating reidentification risk. It is anticipated that the majority of future data used for AI development will be synthetic. 65 High-fidelity synthetic datasets, however, are only as good as the real-world data used to generate them. Biobanks, with their curated specimens and structured metadata, provide the empirical grounding needed to produce synthetic data that preserve clinically meaningful distributions, correlations, and rare events—features that are often underrepresented or distorted in convenience datasets.
ML operations
Importantly, the value of biobanks for AI extends beyond model development into the domain of ML operations, so-called “MLOps,” and post-deployment monitoring. 3 AI models deployed in clinical or operational settings are subject to performance drift over time due to changes in patient populations, testing practices, instrumentation, or clinical workflows. Biobanks can support ongoing model surveillance by serving as stable, reference datasets against which algorithm performance can be periodically reassessed. In this context, banked specimens and data enable reproducible benchmarking, recalibration, and revalidation—functions that are increasingly recognized as essential for maintaining clinical safety, regulatory compliance, and trust in AI-enabled systems.
Collectively, these roles position biobanks not merely as passive repositories of samples but as active enablers of the AI lifecycle—from initial model training and validation to synthetic data generation, to continuous performance monitoring. As health care increasingly integrates AI into diagnostic, prognostic, and decision-support workflows, strategically designed biobanks will be critical to ensuring that these technologies remain accurate, equitable, and clinically meaningful over time.
Challenges
The accelerated pace of AI adoption across scientific disciplines is both highly promising and increasingly concerning. While AI has the potential to transform discovery, diagnostics, and operational efficiency for biobanks, this optimism is tempered by several critical challenges that must be addressed to ensure responsible and effective implementation. Key concerns include evolving regulatory and validation frameworks that often lag behind technological innovation 66 ; unresolved issues surrounding data access, stewardship, and ownership; persistent gaps in AI literacy and acceptance among users; interoperability; and ethics. Moreover, the risk of perpetuating or amplifying existing inequities through biased or unrepresentative datasets remains substantial, particularly when AI systems are developed or deployed without adequate attention to diversity, equity, and inclusion.67,68 Collectively, these challenges underscore the need for deliberate governance, transparent validation, and interdisciplinary collaboration to ensure that the rapid advancement of AI translates into equitable, trustworthy, and scientifically robust outcomes.
Regulatory considerations for AI in biobanking
As biobanks increasingly embed AI in data analytics, sample matching, and predictive modeling workflows, they must navigate a complex and evolving regulatory landscape that governs health data, software tools, and participant rights. In many jurisdictions, AI systems performing tasks akin to diagnosis, risk stratification, or decision support may be regulated as medical devices or software as a medical device, 69 triggering premarket review, performance standards, post-market surveillance, and auditability requirements. 70 In the United States, the process to gain FDA approval is complex, lengthy, and potentially cost-prohibitive. At the same time, the processing of highly sensitive health and genetic data implicates privacy laws such as the Health Insurance Portability and Accountability Act 71 and European Union’s General Data Protection Regulation, 72 mandating data minimization, purpose limitation, explicit consent, data subject rights, and safeguards such as encryption, pseudonymization, and impact assessments. Under proposed or adopted AI‐specific statutes such as the EU’s new AI Act, high-risk systems73,74—including those used in health or the life sciences—may also need to satisfy obligations regarding transparency, risk management, human oversight, logging, and third-party conformity assessments. Furthermore, to maintain reproducibility and trust in AI‐driven decisions, regulators and audit bodies increasingly expect documentation and robust governance frameworks with clearly assigned accountability for any AI system applied to biobank operations or research workflows.
Data access, stewardship, and ownership
It has become increasingly apparent that banked biospecimens—particularly those paired with rich metadata—represent highly valuable assets for industry. From a data-centric perspective alone, the recent $256 million asset purchase of 23andMe’s genetic data by Regeneron underscores the substantial commercial and scientific value of large, well-curated human datasets. 75 Despite this clear value, fundamental questions surrounding data access, stewardship, and ownership remain insufficiently resolved, particularly within academic medical centers and health care systems.
For health care institutions, access to clinical data has become progressively more challenging due to heightened information technology security requirements, privacy regulations, and institutional risk management concerns. 76 Compounding these challenges, EHR systems are fundamentally designed to support clinical care and billing workflows rather than research, resulting in fragmented, incomplete, or difficult-to-extract datasets for secondary use. 75 Therefore, data abstraction and harmonization can be resource-intensive and inconsistent, fueling growing interest in alternatives such as synthetic data to enable AI development while mitigating privacy risk. 65
As biospecimens and associated data acquire explicit monetary value, questions of ownership and control become increasingly complex. Determining who “owns” the data—the patient, the institution, the biobank, or downstream commercial partners—remains contentious and is often constrained by the scope and language of informed consent under which samples were collected. 77 These tensions highlight the need for clearer governance frameworks that define permissible uses, access rights, stewardship responsibilities, and benefit-sharing mechanisms, particularly as academic biobanks navigate partnerships with industry while maintaining public trust and ethical integrity.
Ethics
The use of AI in biobanking raises important ethical challenges related to consent, data ownership, privacy, and fairness. 77 Biospecimens and associated data are often reused in ways not explicitly anticipated at the time of collection, complicating informed consent when AI enables secondary analyses, data linkage, or synthetic data generation at scale.78,79 Concerns also arise around algorithmic bias, particularly when underrepresented populations are inadequately captured in biobank datasets, potentially amplifying health inequities through AI-driven discoveries. 80 Additionally, the opacity of some AI models, especially commercial algorithms, can limit transparency and accountability, making it difficult for participants, researchers, and institutions to understand how data are used and how decisions are derived. 80 Addressing these ethical challenges requires robust governance, dynamic consent models, and continuous oversight to ensure that AI applications in biobanking align with principles of respect, equity, and public trust.
AI acceptance and literacy
Despite the noted use and opportunities of AI in biobanking, gaps in AI data literacy and acceptance remain significant barriers to effective implementation. 81 Many biobank stakeholders—including clinicians, laboratory staff, researchers, and governance bodies—may have limited familiarity with core AI concepts such as data provenance, bias, model validation, and algorithmic drift, leading to skepticism or misplaced trust in AI-enabled tools. This literacy gap complicates informed decision making around consent language, data sharing agreements, and the ethical reuse of biospecimens and associated metadata. At the same time, concerns about data misuse, loss of control, and the “black-box” concept for AI algorithms can undermine participant and institutional acceptance, particularly among historically underrepresented communities. 82 Addressing these challenges requires deliberate investment in education, transparent governance frameworks, and clear communication strategies that demystify AI while reinforcing trust, accountability, and alignment with the core mission of biobanking. 83
Interoperability
Interoperability remains a major challenge for applying AI in biobanking, as biospecimens and associated data are often generated, stored, processed, and governed across fragmented systems.84,85 Variability in laboratory information systems (e.g., Epic Beaker vs. Cerner LIS), biobank management platforms, EHRs, and testing or imaging pipelines leads to inconsistent data models, terminologies, and metadata quality that hinder seamless data integration for AI use. Even when technical standards exist, uneven adoption and local customization limit cross-institutional harmonization and model portability. These interoperability gaps increase the cost and complexity of AI development, constrain multisite learning, and reduce the generalizability of AI models—underscoring the need for standardized data schemas, shared ontologies, and interoperable governance frameworks tailored to biobanking environments.
Limitations
Limitations of this review include the accelerated pace of AI adoption across many fields including biobanking. We have seen these technologies move from novelties to transformative approaches in a matter of months. Similarly, these same technologies have revealed new challenges and problems following adoption. As such, this review represents a snapshot of the current state today and will no doubt evolve as technology improves, regulations “catch-up,” and social, economic, and ethical considerations are addressed.
Conclusions
AI is no longer a peripheral innovation in biomedical research—it is rapidly becoming a core driver of discovery, efficiency, and education. Biobanking, as the bridge among biospecimens, data, and translational research, is uniquely positioned to both enable and benefit from this transformation. Early successes in areas such as automated QC, metadata extraction, and privacy-preserving synthetic data generation demonstrate the feasibility and value of AI integration across diverse facets of biobank operations. At the same time, AI offers opportunities that extend well beyond operational gains. Multimodal data integration is beginning to reveal novel biomarkers and therapeutic targets, while generative models and large language systems are opening new avenues for collaboration, education, and protocol development. These capabilities shift biobanking from a traditionally passive infrastructure into an active, intelligent partner in biomedical research.
Looking ahead, the challenge will be to harness AI responsibly and equitably. Issues of data quality, interoperability, privacy, and governance must remain central to the development and deployment of AI-enabled biobanking systems. Equally critical is the need for ongoing education and workforce training, ensuring that biobankers and researchers alike can engage with these technologies effectively and ethically. Ultimately, the convergence of AI and biobanking promises to accelerate translational research, improve global health equity, and shape the next generation of precision medicine. By embracing AI thoughtfully, biobanks can evolve into dynamic engines of innovation—transforming how biological materials and data are curated, shared, and leveraged for scientific and clinical impact.
Author Contributions
Dr. Tran drafted the manuscript, Drs. Rashidi and Dhir provided feedback and helped copyedit the manuscript.
Footnotes
Author Disclosure Statement
Drs. N.K.T. and H.R. are coinventors of the Machine Intelligence Learning Optimizer (MILO) automated ML software, a University of California equity-owned start-up company, and cofounders of MILO-ML, Inc.
Funding Information
No funding was received for this article.