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      Zifo's AI-Powered Clinical Reporting Solution Named Runner-Up in the 2026 CDISC AI Innovation Challenge

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      News provided by

      Zifo

      Sep 25, 2026, 09:00 ET

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      Recognition underscores Zifo's continued leadership in applying AI, clinical expertise, and data standards.

      CAMBRIDGE, Mass. and CAMBRIDGE, England, Sept. 25, 2026 /PRNewswire/ -- Zifo, a leading global enabler of AI and data-driven enterprise informatics for science-driven organizations, has been named runner-up in the 2026 CDISC AI Innovation Challenge for TLFGenix, its AI-driven solution for generating clinical trial tables, figures, and listings.

      Continue Reading
      Zifo's solution is an end-to-end AI pipeline that takes SAP and ADaM datasets as inputs and produces submission-ready TLF outputs with complete traceability and human approval at every critical stage.
      Zifo's solution is an end-to-end AI pipeline that takes SAP and ADaM datasets as inputs and produces submission-ready TLF outputs with complete traceability and human approval at every critical stage.

      TLFGenix was recognized in Use Case 3: AI-Driven Tables, Figures, and Listings Generation, one of three use cases featured in this year's global competition. The use case challenged participants to apply AI to automate the creation of clinical outputs while maintaining an auditable connection from study objectives and statistical analyses through to final tables, figures, and listings.

      The recognition marks the second consecutive year that Zifo has been honored in the CDISC AI Innovation Challenge. In 2025, Zifo was named runner-up for solutions addressing both Unified Study Definitions Model-driven study design and semantic traceability across the clinical data lifecycle.

      Advancing Standards-Based Automation in Clinical Research

      Tables, figures, and listings are essential outputs in the statistical analysis and reporting of clinical trial results. Their production typically requires specialized programming, detailed interpretation of study documentation, and extensive review to ensure accuracy, consistency, traceability, and compliance.

      Zifo's TLFGenix is an end-to-end AI pipeline that converts Statistical Analysis Plans and ADaM datasets into traceable tables, figures, and listings. Six specialized agents digitize the SAP into a structured logical model, profile ADaM data, generate an ARS-aligned analysis specification, produce R driver scripts, execute them in a network-isolated sandbox, and check schemas and output consistency. Statisticians approve the analysis specification before code generation and sign off on each TLF. All models run on secure internal infrastructure, so clinical data remains within the enterprise boundary. A lineage graph connects objectives, endpoints, analyses, and results; when an SAP or approved specification changes, it identifies affected outputs for selective regeneration and reapproval. Each TLF includes a structured evidence record containing the traceability matrix, execution logs, generated code, and approvals. This approach reduces repetitive programming and manual errors while preserving expert oversight and auditability.

      By connecting study objectives, analysis requirements, metadata, and CDISC-aligned data standards, the solution is designed to support the efficient generation of reviewable and traceable clinical outputs. The approach brings together automation and human expertise rather than treating AI as a replacement for scientific or statistical judgment. This allows clinical teams to reduce repetitive effort while retaining the oversight, transparency, and control required in regulated environments.

      "TLFGenix is part of our end-to-end AI Clinical workbench, a key component in Zifo's Practical AI initiative, focused on helping the industry adopt AI across the value chain through real-world use cases with clear, measurable value. It combines AI, CDISC standards, and proven statistical programming practices to generate traceable tables, figures, and listings from study specifications while keeping humans firmly in control of review and decision-making. We are pleased to see this practical, ROI-led approach recognized by CDISC for the second time," said Deepak Ananthan, Head of Clinical Services and AI Scaling at Zifo.

      The 2026 CDISC AI Innovation Challenge attracted 50 submissions from 30 organizations and individual entrants, more than doubling the 22 submissions received in the inaugural 2025 competition. Submissions were independently evaluated on innovation and relevance, technical quality and feasibility, and standards integration, traceability, and impact.

      Zifo also submitted an AI-enabled approach to synthetic data generation for automation testing. The solution focused on creating realistic SDTM and ADaM datasets with clear traceability to source inputs and metadata, reflecting the company's broader commitment to applying AI and clinical data standards across the research lifecycle.

      Building on Zifo's 2025 Recognition

      Zifo's latest achievement builds on its strong showing in the inaugural CDISC AI Innovation Challenge.

      In 2025, Zifo was recognized for two solutions addressing critical points in the clinical research lifecycle. The first transformed clinical study protocols into structured, compliant Unified Study Definitions Model outputs with validation and traceability. The second connected protocols, case report forms, SDTM, ADaM, tables, figures and listings, Biomedical Concepts, and USDM within an interactive clinical data lineage environment.

      With TLFGenix, Zifo extends this standards-based innovation further into clinical analysis and reporting. The solution aligns with the broader industry objective of creating a more connected clinical data pipeline in which information can flow from protocol and study design through data collection, analysis, and reporting with greater automation and traceability.

      Supporting CDISC 360i and a Connected Clinical Data Pipeline

      The 2026 AI Innovation Challenge was aligned with CDISC 360i, an initiative exploring how standards, technology, and automation can support a connected, end-to-end clinical research data flow.

      According to CDISC, the Challenge provides a collaborative platform for vendors, researchers, pharmaceutical organizations, and other innovators to explore how AI and machine learning (ML) can advance the adoption and impact of clinical research data standards. The program was launched in 2025 and continues as an annual initiative.

      Zifo will have the opportunity to showcase TLFGenix to the broader clinical data community in connection with the 2026 CDISC US Interchange in Denver this October, where winning and runner-up solutions from the Challenge are expected to be presented.

      About Zifo 

      Zifo is the leading global enabler of AI and data-driven enterprise informatics for science-driven organizations. With extensive solutions and services expertise spanning research, development, manufacturing, and clinical domains, we serve various industries, including pharma, biotech, chemicals, food and beverage, oil and gas, and FMCG. Trusted by over 190 science-focused organizations worldwide, Zifo is the partner of choice for advancing digital scientific innovation. For more information, visit https://www.zifo.com.

      About CDISC

      CDISC is a 501(c)(3) global non-profit charitable organization that streamlines research and enables connections to healthcare through the development of clinical research data standards. CDISC has developed a suite of standards to support clinical research from protocol through analysis and reporting. CDISC standards make it possible for data to speak the same language, empowering simple data collection and private sharing that makes the most of the valuable information offered by patients participating in research studies around the globe. https://www.cdisc.org

      SOURCE Zifo

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