As we move through 2025 it’s clear that the biometrics industry is set to continue to transform at pace, with four key trends emerging that look set to shape the landscape over the coming months and years. Whether it’s adopting the latest generative AI strategies and automation, utilizing open-source platforms or simply investing in agile, high-quality teams, organizations will need to think and plan ahead if they are to remain competitive
At Veramed, we pride ourselves on always being at the forefront of industry developments. To this end, we’ve created the VeraTech Radar – a downloadable asset that talks to the latest trends within Biometrics. You can sign up HERE.
So, what’s on our radar right now? In this blog we explore what we can expect to influence the biometrics industry in 2025 and beyond.
Open-Source and Multi-Lingual Submissions
Pilot / Adopt
The use of Open-Source in the field of clinical studies isn’t a new concept, and over time, the recognition of the benefits of these technologies has led to increased adoption.
Biometrics technologies and solutions being shared openly through open-source platforms enables greater collaboration, standardization and innovation within the industry as it allows developers and researchers to freely access, modify and contribute to the codebase and algorithms. The transparent nature offered by Open-Source helps to build trust and credibility in trial results, while customization options enable researchers to modify and adapt the technology to meet the specific requirements of their trial, improving the flexibility and effectiveness of the trial process.
R Language is just one example of Open-Source Programming language that is being rolled out for use in the submission of clinical trial data.
End-to-End Standards-Based Automation
Proof of Concept / Pilot
Within the biostatistics industry, end-to-end standards automation plays a vital role in streamlining processes related to data collection, analysis and interpretation. By adopting standardized protocols and guidelines for data management, statistical analysis and reporting, biostatisticians can better ensure the quality, integrity and interpretation of their research outcomes. It also makes it far easier to facilitate collaboration across multidisciplinary and/or geographically diverse teams.
Standardization also simplifies adherence to industry-recognized standards and enhance data integrity, regulatory compliance and cross-study comparability in clinical trials and other studies. This will make it easier to make accurate clinical decisions based on high quality statistical evidence.
Example initiatives:
- TransCelerate DDF collaboration is driving Digital Protocol and the USDM data standard.
- The CDISC 360i project aiming to implement and demonstrate end-to-end automation.
- CDISC Biomedical Concepts (BC) Implementation across foundation standards – from protocol to results.
Generative AI Strategies and Change
Strategy / Assess
Artificial Intelligence (AI) is starting to become an integral part of the way many organizations operate, whether it’s the use of smart chatbots to answer customer queries or to analyze data to create personalized treatment plans for patients. However, when it comes to clinical trials, the use of AI strategies is very much still in their infancy, with organizations exploring how and where they might use it to enhance their current and future operations.
Generative AI strategies refer to approaches used in artificial intelligence to create new data or content based on the existing input. These strategies usually involve using algorithms to generate the new information, based on the input data that it was trained on. Generative AI strategies can be used across a variety of industries including biometrics, where they can be used to generate synthetic healthcare data for research purposes. This technology will allow researchers to generate realistic synthetic datasets that preserve the statistical properties of real-world data without compromising patient privacy.
Some of the benefits of this approach include:
- Overcoming data limitations.
- Improved data sharing for better collaboration.
- The ability to test statistical models in a safe and controlled environment.
As we progress into 2025, we can expect to see more exploration into AI strategies and testing proof of concept before it’s widely adopted.
Adopting Agile Methodology
Proof of Concept / Pilot
Why have a regular team when you could have a high-performing team of individuals with the necessary technical expertise and experience to optimise your outcomes?
With increasing pressure to deliver the most accurate data at speed, in order to facilitate data-driven decision making for informed choices and precise strategies, organizations must look to CROs that have a deep understanding of the latest biometrics tools and technologies, and that can utilize these to their advantage. This could include adopting collaborative technologies to bring together remote teams or using automation in repetitive tasks to free up a human workforce for more strategic work.
Agile methodology, with its focus on iterative development, adaptability and continuous improvement, isn’t a new approach to working, but its place in the biometrics industry is only just starting to gain pace. By leveraging the principles of Agile – which naturally follows the adoption of Open-Source programming – clinical trial teams can enhance their efficiency and responsiveness in managing complex trial processes and quickly adapt to changing requirements. This tried-and-tested approach offers opportunities for improved collaboration, faster decision making and better alignment with patient needs – all of which can enhance the speed, flexibility and quality of clinical research outcomes.
The biometrics industry is on the brink of a transformative period, where innovation and AI are set to redefine how we collect, analyze and use data to inform decision-making and improve healthcare outcomes. By staying informed and adapting to these trends, you can make significant strides towards playing a pivotal role in bettering global health initiatives and addressing complex publish health challenges.
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Stuart Malcolm
Head of Standards, Efficiency & Automation
With over 25 years’ experience developing software, Stuart is responsible for driving the development and adoption of software, tools and techniques that optimize Veramed’s clinical trial analysis projects.
