January 28, 2022
Artificial intelligence (AI) is radically transforming biobanking and modern research. Traditional methods of harnessing data and manual decision-making processes have been replaced with AI-driven algorithms and machine learning. Biospecimen collection and storage are equally shifting from manual to automated processes driven by artificial intelligence. This is not only accelerating clinical research and drug development but also resulting in improved therapies and better patient outcomes.
Biobanks play a pivotal role in the clinical research process. They handle biological specimens that are used by clinical researchers to provide unique insights into genetic factors underlying diseases, identify potential drug targets, and facilitate personalized treatment. Biobanks also store vast amounts of information related to the biological samples they store. This information can be used for further medical research and drug development.
Modern medicine presents unique opportunities for AI-driven biobanking through the use of robots, cloud computing, and other technical advancements. AI has paved the way for virtual biobanks that provide a single point of access to a network of biospecimens, facilitating transnational collaboration.
AI can help ensure optimal utilization of samples in biobanks. If an AI-powered system is connected to freezers, the system automatically alerts biobank managers as soon as there is vacant space. This makes it possible to maximize storage space utilization in biobanks through sample relocation.
Furthermore, AI can help determine the quality of DNA samples through the analysis of DNA gel electrophoresis pictures. It can also help determine the percentage of malignancy in tissue samples through automated analysis of histopathology images.
AI enables biobanks to integrate genomic data with existing health information and provides a way of understanding the impact of genetic variation on human health. AI-generated algorithms play a crucial role in solving data-related issues, preventing data redundancy, and identifying missing data, if any. This helps create a mathematical model that can be used for the diagnosis and prognosis of a disease from a given data set. AI use in biobanking also makes it possible to create phenotypic models of disease. Machine learning makes it possible to create a mathematical model to discover disease outcomes that would easily escape or go unnoticed by the traditional statistical approaches.
AI also makes it possible to process large volumes of data generated by modern biobanks. Unlike manual methods, AI-generated computations are not affected by human errors. In a recent study conducted by the UK Biobank, machine learning was utilized to determine COVID-19 mortality risks in patients and stratify high-risk patients. These risk assessment tools can help commence proper treatment for high-risk patients and decrease mortality rates.
Precision medicine, also known as personalized medicine, is an emerging field in modern medicine that is concerned with understanding how an individual’s genetic makeup and lifestyle affect their treatment outcomes. AI forms the basis of precision medicine and creates opportunities for identifying risk factors for various diseases and leveraging that information to create individualized plans for disease therapy and prevention. Disease-specific biobanks support genotype-driven participant recruitment for clinical research. This provides data needed for driving personalized medicine.
Modern biobanks face numerous challenges relating to the biospecimens they store and the associated metadata. They need to ensure that the biospecimen quality is uncompromised throughout the sample lifecycle. They also need to ensure that patient-related data is accurate and is not accessible to unauthorized parties. Artificial intelligence can be used to process large volumes of data concurrently while maintaining accuracy. AI-generated algorithms form the basis of predictive medicine and hence improve efficiency and decision-making in biobanks. AI helps in the diversification of biobanks and improves data sharing across biobanks.
A biospecimen management system, also known as a Laboratory Information Management System (LIMS), can be used to support AI-powered biobanking. A biospecimen management system electronically stores and manages data in a structured manner which is required to feed IoT-powered smart devices and sensors. A biospecimen management system helps automate the process of biobanking and manages biospecimens throughout their entire lifecycle. It eliminates the need for manual inputs and hence reduces the chances of human errors. It enables a seamless flow of information among relevant stakeholders while ensuring compliance with regulatory requirements.
A biospecimen management system can provide real-time data on various biobanking processes to support AI-driven decision-making. For example, a biospecimen management system can be integrated with IoT-powered temperature monitoring systems to record data related to freezer temperature which is crucial to maintain biospecimen quality and integrity. This helps initiate necessary actions, such as sending alerts to biobank managers, if the freezer temperature changes significantly. The biospecimen management system can further be integrated with AI-driven data analyzers to analyze temperature fluctuations over a time range and determine their root cause to prevent recurrence of such incidences in the future.
A biospecimen management system helps manage biobank inventory and triggers alerts if the quantity of inventory items falls below a certain threshold quantity. In the future, it may be possible to integrate a biospecimen management system with AI-powered ordering systems to place orders for depleting inventory items automatically. This will help ensure sufficient availability of inventory items at all times, preventing any hindrances to biobanking activities due to lack of inventory items. AI is gradually making inroads into biobanking. There are many such ways in which AI is likely to transform biobanking.
Biobanking has gone through significant changes in the last decade, having embraced technological advances. Currently, the biggest accelerator for rapid change is artificial intelligence. AI allows disease-specific biobanks to utilize genomic data to design disease models and predict outcomes of several life-threatening, genetic, and infectious diseases. AI-generated algorithms process large volumes of data, speeding up clinical research and drug development. With artificial intelligence, modern biobanks can accelerate their processes, ensure compliance with regulatory requirements, improve decision-making, and hence provide superior patient outcomes. A LIMS complements the operations of AI-driven biobanks to guarantee accurate and timely disease interventions.