Fujitsu and THERS Successfully Deploy AI to Accelerate Clinical Trial Recruitment and Combat Drug Loss in Japan
Key Insights
Fujitsu (search) and Tokai National Higher Education and Research System (search) (THERS (search)) have completed successful field trials using generative AI to structure clinical data, achieving approximately 90% accuracy with 1,800 breast surgery patient records.
The AI-powered approach identified 42 potential clinical trial candidates with 27 being actually eligible, potentially reducing patient selection time by approximately one-third and accelerating clinical research processes.
This initiative directly addresses Japan's "drug loss" problem—the lack of availability of drugs used overseas due to stringent local approval requirements—by streamlining clinical trials and improving real-world data utilization.
Fujitsu (search) Limited and Tokai National Higher Education and Research System (search) (THERS (search)) announced today the successful completion of field trials leveraging generative AI to streamline clinical trial participant selection. The initiative represents a significant step toward addressing Japan's "drug loss" challenge—a situation where medications available internationally remain inaccessible in Japan due to the country's rigorous approval requirements.
The field trials focused on processing unstructured clinical data, which has traditionally required labor-intensive manual handling. Using approximately 1,800 patient records from breast surgery procedures at Nagoya University and Gifu University, the AI system successfully structured previously unstructured data with approximately 90% accuracy.
When applied to three clinical trial projects, the newly structured data helped identify 42 potential candidates, with 27 proving actually eligible upon further review. This approach potentially reduces patient selection time by approximately one-third, enabling faster decision-making and improving patient access to appropriate clinical trials.
AI-Powered Clinical Data Processing
In clinical settings, patient data exists in two primary forms: structured data (organized, quantifiable information like vital signs and lab results) and unstructured data (information lacking predefined format, such as physicians' notes). The latter has historically presented significant challenges for efficient analysis and utilization.
"Converting unstructured clinical data into structured formats has been a major bottleneck in clinical research efficiency," said a representative from the research team. "Our AI approach dramatically accelerates this process while maintaining high accuracy."
The technology's 90% accuracy rate in structuring previously unorganized clinical information represents a substantial improvement over traditional manual methods, which are not only time-consuming but also prone to human error and inconsistency.
Enhancing Clinical Trial Recruitment
Patient recruitment remains one of the most challenging aspects of clinical trials globally, often causing significant delays in drug development timelines. The AI system's ability to reduce selection time by approximately one-third addresses this critical pain point.
By screening patient records more efficiently, the system enables researchers to:
- Identify suitable candidates more quickly
- Reduce administrative burden on clinical staff
- Potentially increase trial participation rates
- Accelerate overall clinical development timelines
The successful identification of 27 eligible candidates from an initial pool of 42 potential participants demonstrates the system's effectiveness in pre-screening, significantly reducing the manual review workload.
Addressing Japan's "Drug Loss" Challenge
Japan's pharmaceutical landscape has long struggled with "drug loss"—a phenomenon where medications widely used internationally remain unavailable to Japanese patients due to stringent local regulatory requirements and the challenges of conducting clinical trials in the country.
This collaboration between Fujitsu (search) and THERS (search) directly addresses this issue by making clinical trials more efficient and feasible within Japan. By streamlining the patient selection process and improving data utilization, the initiative aims to make Japan a more attractive location for international clinical research.
"This technology has the potential to significantly reduce the gap between drug availability in Japan and other developed nations," noted a project spokesperson. "By making clinical trials more efficient, we can help ensure Japanese patients gain access to innovative therapies more quickly."
Future Expansion Plans
Building on these promising results, Fujitsu (search) and THERS (search) have outlined ambitious plans for expansion. The organizations will work to:
- Increase the number of target diseases beyond breast surgery
- Expand participating medical facilities
- Further improve the AI system's accuracy
- Implement the approach in actual clinical trials
Additionally, the partners will integrate these capabilities with Paradigm Health (search), Inc.'s clinical trial platform to accelerate real-world data utilization. This integration will facilitate collaboration between medical institutions and pharmaceutical companies, enhancing both the planning and execution of clinical trials.
Technology Implementation Timeline
On May 30, Fujitsu (search) will expand its Healthy Living Platform by launching a new function that promotes the structuring and utilization of medical data using its AI service, Fujitsu Kozuchi (search). Future plans include linking this function with Fujitsu's enterprise large language model (LLM) Takane (search) to further enhance data analysis capabilities in clinical research.
THERS (search) aims to leverage these technological advancements to attract international joint clinical trials to Japan's Tokai region, improving patient care through operational efficiency and high-quality clinical data management.
Broader Impact on Japanese Healthcare
This initiative aligns with Fujitsu (search)'s Uvance business model, which focuses on addressing societal challenges through technological innovation. By improving clinical research efficiency, the project contributes to the broader goal of enhancing healthcare delivery and pharmaceutical development in Japan.
The successful implementation of AI in clinical trial participant selection represents a significant step toward modernizing Japan's clinical research infrastructure, potentially reducing the time and cost associated with bringing new medications to Japanese patients.
As the project expands to include more diseases and facilities, its impact on addressing drug loss in Japan is expected to grow, potentially transforming the country's position in global pharmaceutical research and development.