Saama, a top provider of AI-based solutions in the Life Sciences industry, has launched its Tables, Listings, and Figures (TLF) Analyzer. This tool works with Saama’s AI-powered document generation platform to make clinical study report writing more efficient and effective. The TLF Analyzer uses advanced multi-modal AI models that can understand text, numbers, and graphs. It can automatically analyze and summarize complex clinical data from tables, listings, and figures in one process. In the past, reviewing TLFs was a slow and error-prone process that took weeks and involved multiple teams. Saama’s solution cuts down these delays by offering quick, context-aware insights based on the study protocol and statistical analysis plan.
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“Our TLF Analyzer is changing the way clinical reporting is done. The Figures-to-Text feature is what makes it unique,” said Prasanna Rao, Chief Products and Innovation Officer at Saama. “Most tools focus only on tables and listings, but we have developed models that understand and summarize graphs as well. This lets users turn complex visual data into accurate, clinically relevant stories, giving our clients a big advantage. It makes writing clinical study reports faster, more accurate, and in line with scientific standards.”
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The TLF Analyzer’s Figures-to-Text feature reads graphical outputs like Kaplan-Meier curves, forest plots, waterfall plots, and box plots. It creates text summaries that match the statistical data in tables. This ensures precision and regulatory traceability while cutting down the time needed for data interpretation. What used to take two to three weeks can now be done in three to four days. Early use of the TLF Analyzer shows a 60 to 70 percent drop in manual analysis time, allowing clinical teams to focus more on insights than on data processing.
The platform fully supports tables, listings, and figures with intelligent search, customizable prompts, and interactive AI refinement. Key benefits include faster report writing by summarizing TLFs in minutes, context-aware outputs that include knowledge from the study protocol and statistical analysis plan, NLP-powered search for relevant data even in complex or unlabeled files, one-click options to tweak summaries, and easy integration into clinical study reports with traceable citations for compliance. By looking at efficacy, safety, and demographic data together, it ensures consistent, clinically meaningful narratives, improving the quality and efficiency of reporting.
Looking ahead, Saama plans to expand the Figures-to-Text feature to include pharmacokinetic and pharmacodynamic analyses, improve cross-study insights using Agentic AI, and move toward fully automated CSR generation. This innovation is expected to reshape clinical reporting by cutting manual effort, speeding up timelines, and keeping scientific standards high throughout the process.
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