ACL Digital Life Sciences Transforms Scientific Graph Editing with GenAI for a Global Pharmaceutical Leader
Overview
A global pharmaceutical leader specializing in innovative medicines, breakthrough therapies, and advanced healthcare solutions sought to streamline the way scientific data is visually presented. With a robust portfolio spanning diabetes, oncology, immunology, and neuroscience, the company emphasizes accuracy and clarity in its scientific communications. However, the process of editing and standardizing graphs in research documents remained a significant operational bottleneck.
ACL Digital Life Sciences partnered with the organization to modernize and automate this critical workflow, leveraging the advanced and transformative power of GenAI technologies. The company also addressed a traditional manual, error-prone process with precision automation, empowering scientific teams to focus on what matters most: discovery and innovation.
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Challenges
Scientific research documents often include a wide array of charts and graphs that are vital for communicating findings. However, the existing approach created issues that collectively impacted operational efficiency, publication timelines, and internal quality assurance.
Graphical Inconsistencies
Variations in orientation, font styles, scaling (maximum coordinates), and color schemes reduced clarity and professionalism
Manual Revisions
Traditional editing processes were labor-intensive and time-consuming, with no standardized method for validation or quality control
Complex Dataset Management
Regenerating uniform graphs from an entire set of PDFs or images added complexity, slowing production cycles
Solution
- Automated Graph Regeneration Using GenAI: Implemented the YoLo model to process complete sets of images and PDFs, automatically regenerating graphs with uniform orientation, font styling, scaling, and color schemes.
- Consistent Visual Standards: Leveraged AI to detect and correct inconsistencies, ensuring every regenerated graph adhered to predefined visual standards.
- UI-Driven Reverification Workflow: Integrated a manual review interface enabling side-by-side comparison of original and regenerated graphs. Enabled manual approval for enhanced accuracy and compliance.
Outcomes
The GenAI-driven solution delivered significant operational and quality improvements:
60% Reduction in Processing Time
Automated graph editing dramatically reduced turnaround time, enabling faster publication and review cycles
95% Styling Consistency
Achieved near-total uniformity in graph formatting, enhancing clarity and credibility in scientific documents
40% Reduction in Manual Verification Workload
High model accuracy minimized the need for human review, freeing up expert resources for higher-value tasks
Enhanced Workflow Efficiency
Seamless integration of automation with manual review delivered a scalable, repeatable, and high-accuracy process
