In today’s world, it’s nearly impossible to imagine an advanced medical device without software at its core. Whether the software is integrated into the device (SiMD) or is the device itself (SaMD), it is the driving force behind groundbreaking innovations that enhance patient care and operational efficiency. However, developing software for medical devices is a complex and rigorous process, as it must comply with strict regulatory standards to ensure safety and effectiveness. To understand the challenges and how we can address them, let’s explore the role of large language models (LLMs) in transforming this process.
Enhancing QMS Efficiency with LLMs
Creating software for medical devices requires careful planning and extensive documentation. This documentation is managed through a quality management system (QMS)—a formalized framework outlining the structure, processes, roles, and procedures needed for effective quality control. A QMS includes key policies, procedures, forms, work instructions, and records that provide evidence that the system is being followed.
For medical device manufacturers, it is essential that the QMS aligns with international standards to meet regulatory requirements in different markets. This results in a labor-intensive process, generating vast amounts of documentation that is often difficult to manage efficiently.
Large language models (LLMs) have emerged as a transformative solution to manage exactly this type of challenge. They can process and generate large volumes of unstructured text quickly, offering the potential to reduce time that manual document creation takes and to enable real-time updates as global and regional regulations evolve.
Imagine a system where a chain of AI agents works together seamlessly: one monitors and references regulatory guidelines; another compares these guidelines to the specific needs of a device; a third uses examples of past QMS documents to create new ones; and yet another analyzes design requirements to propose a risk matrix. Additionally, different AI Agents could be dedicated to different document types—such as the overall QMS, standard operating procedures, or software development plans—all working in concert to produce a fully compliant system.
Reducing QMS Development Time: AI-Driven Innovation in Medical Software
Traditional QMS development processes can take weeks or even months, especially in the case of complex medical software projects. From the initial document creation to full QMS implementation, the timeline can range from three to nine months. Given the complexities of regulatory compliance, this process is ripe for innovation.
In life sciences, LLM-powered solutions have already been demonstrated to deliver significant time savings. For instance, leveraging its Globant Enterprise AI platform, Globant developed an AI Agentic solution to produce compliant marketing copy for Organon LAMEX, a leader in the pharmaceutical industry, resulting in 80% efficiency gains. This success suggests that a similar strategy could greatly accelerate the creation of a compliant QMS for medical device software.
A Step Toward Revolutionizing QMS Development
Admittedly, integrating LLMs into QMS development is not without challenges. Accurately interpreting complex regulations and ensuring the precision of generated documents remain significant concerns. Although advances in LLM technology have improved performance, human oversight will still be essential to guarantee quality and compliance. Nonetheless, the potential benefits are substantial, and the time to begin piloting this innovative approach is now.
By embracing these strategies, businesses can move beyond traditional documentation methods and harness the full analytical and predictive power of LLMs, ultimately accelerating the development of safe and effective medical software. Discover more about LLMs applications in Healthcare & Life Sciences here.
