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TCS ADD™
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TCS ADD™: A Suite of Modern and Open Technology Platforms
TCS ADD™ is a suite of modern and open technology platforms for clinical research and drug development that helps make clinical trials more agile and safe.
Related Insights
This paper highlights challenges in data transfer agreement and demonstrates how the proposed e-DTA solution can transform the clinical trial industry.
White Paper
18 Dec 2024
Learn how an AI-based solution can reduce clinical data reporting turnaround time to minutes and manual efforts drastically during clinical data management.
White Paper
02 Sep 2024
Learn how conversational AI can address clinical regulatory intelligence challenges and help life sciences organizations gain insights and ensure compliance.
White Paper
29 Aug 2024
Smart bio research
Medical research today is growing at an unprecedented pace, with millions of new articles published every year and vast repositories like PubMed hosting tens of millions of abstracts. For researchers and healthcare professionals, this abundance of information is both a blessing and a challenge. Finding the right studies, extracting meaningful insights, and validating safety data often feels like searching for a needle in a haystack. Traditional methods rely heavily on manual review, which is slow, repetitive, and prone to error. These inefficiencies create significant bottlenecks in drug development, clinical trials, and regulatory compliance, where timely decisions can have a direct impact on patient safety.
The whitepaper explores how AI and Gen AI can transform this process. By leveraging advanced large language models trained on biomedical data, the proposed solution automates literature reviews, summarises research papers, and extracts key findings with remarkable accuracy. Instead of spending hours reading and cross-referencing, researchers can now pose natural language queries and receive context-aware responses. The system integrates APIs from PubMed Central for article retrieval and uses BioC for structured text extraction. For handling multiple documents, it utilises a vector database that stores content as embeddings, allowing for semantic search and linking related research findings. This architecture ensures scalability and speed, turning raw data into actionable insights.
AI for drug safety
One of the most impactful applications is in pharmacovigilance, where safety signal detection is critical. The AI-driven workflow classifies literature into valid Individual Case Safety Reports (ICSRs), potential ICSRs, and non-ICSRs, thereby reducing manual intervention while maintaining accuracy and precision. This not only accelerates compliance processes but also strengthens drug safety monitoring.
The benefits are clear: review times are drastically reduced, precision is enhanced through semantic understanding, and scalability enables the simultaneous processing of thousands of articles. These improvements support essential functions like clinical trial design, post-market surveillance, and regulatory policymaking. As the healthcare analytics and life sciences markets continue to grow rapidly, such solutions are becoming indispensable.
Beyond literature
Looking ahead, the approach can be extended across domains, incorporating real-time literature analysis and more advanced contextual AI capabilities. The integration of AI and GenAI in medical research is not just a technological advancement; it is a necessity for keeping pace with the ever-expanding body of scientific knowledge. By addressing inefficiencies and reducing costs, these innovations ensure timely access to accurate insights, ultimately improving patient safety and accelerating clinical research.
The white paper was presented at PharmaSUG 2025 in the U.S., showcasing how AI and gen AI revolutionise medical literature review, accelerating insights and improving drug safety.
Dive deeper into the insights! Click here to explore the full PharmaSUG 2025 paper and uncover the complete context behind this excerpt.
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