New data show that applying AI to the migration of translated COAs into eCOA platforms can meaningfully reduce errors, accelerate localization workflows, and support broader global patient participation—while still relying on human reviewers to ensure linguistic precision and clinical integrity.
Behind every successful global trial are the tools that give patients a voice; translated, localized, and adapted so their experiences are understood exactly as intended, in every language and culture. YPrime’s new research explores how artificial intelligence (AI) can improve the migration of translated clinical outcome assessments (COAs) into eCOA systems, reducing human-introduced errors and accelerating study startup. The findings show that AI-assisted migration can meaningfully shorten localization timelines while keeping human linguistic oversight where it matters most.
AI isn’t replacing linguists, it’s empowering them. By automating the most error-prone, technical parts of the localization process, AI allows linguists to focus on the work that matters most: refining meaning, cultural nuance, and clinical accuracy. The result is faster, cleaner, and more reliable localized content that supports truly global participation in clinical research.
I recently presented this research as a poster at ISPOR, where industry leaders gathered to explore how technology and innovation are advancing the quality and accessibility of clinical trials worldwide. The response reinforced how critical localization has become in ensuring equitable global participation and faster study activation.
At ISPOR, I unveiled findings from a 15-study analysis covering 11 unique languages. Across the dataset, AI reduced total migration errors by 74%, cut the number of screen reports with errors by 60%, and shortened the average localization review process by 67%.
As clinical research has become increasingly global, the localization industry that supports it has evolved in parallel. What began as a specialized service to meet the language needs of multinational studies has grown into a complex, data-driven discipline central to trial success. Today, a single study can require twenty or even upwards of fifty languages, each adding new layers of coordination, review, and potential delay.
eCOA localization isn’t just about translation, it’s about accuracy, cultural adaptation, and regulatory compliance. When done well, it ensures clinical trial participants understand each question exactly as intended. When done poorly, it can delay approvals, distort data, or even exclude patient populations entirely.
Earlier this year, I shared in an article with Applied Clinical Trials where I stated, “If you’re speaking to more patients in more places, you need more languages—and that means localization has to scale without slowing the trial down.” The challenge for sponsors and vendors alike is how to do that without extending timelines.
When people think about eCOA localization challenges, they often focus on linguistic validation or eCOA proofreading. But a key bottleneck sits between these two steps—migration, when translated COA content is imported into an eCOA platform.
Migration involves mapping each translated text element, including questions, instructions, and response options, into the correct place within a technical file. This may sound simple, but differences in file structure, formatting, and HTML tags across languages make it a highly error-prone, manual process.
A misplaced tag can change how a question is displayed or interpreted, altering the measurement properties of the COA. For example, if formatting tags or response structures are misaligned, patients may perceive symptom severity scales differently, introducing subtle but significant variability into the data. These errors ripple through the entire review cycle, requiring additional rounds of proofreading, regeneration, and reapproval.
Because migration sits at the midpoint of the localization workflow, errors introduced there can cascade through the remaining—and most time-sensitive—steps of proofreading, linguistic review, and approval. By improving accuracy at this stage, AI reduces burden in the downstream processes that most directly affect submission timelines.
In the poster, I discussed how our goal was to test whether AI could reduce human-introduced errors during migration, resulting in fewer review rounds and faster approval of localized eCOA screen reports.
The hypothesis: AI, trained on both the specific file structures and rules of an eCOA system, with an understanding of language, can manage the technical aspects of migration more accurately than humans—freeing linguists to focus on context, meaning, and overall quality.
Using a sample of 15 studies spanning 11 languages, YPrime compared two localization workflows to assess whether AI could reduce human-introduced errors during eCOA migration.
In the human-led workflow, translated COA text was manually imported into the eCOA system. Errors identified during Round 1 proofreading were documented and categorized to establish a baseline of human-introduced issues.
In the AI-led workflow, the same files were re-migrated using YPrime’s proprietary AI Migration Tool, which is trained on the company’s specific eCOA file structures and tagging logic. The resulting AI-generated outputs were then compared directly against the human baseline to determine whether the previously observed errors persisted—and whether any new or unique errors were introduced.
The comparison evaluated:
The results were both encouraging and practical:
For sponsors, fewer review rounds mean earlier delivery of certified screen reports, a prerequisite for releasing localized study builds and initiating site activation. In a multi-country study, this can accelerate first-patient-in by weeks, directly impacting overall trial timelines and patient access.
Localization delays don’t just affect schedules; they affect representation. When timelines tighten, underrepresented languages are often the first cut from scope. But removing a language doesn’t only exclude patients in the regions where that language originates, it also excludes people who speak that language anywhere in the world, including within major research markets like the U.S. and Europe.
Improving the speed and accuracy of localization enables sponsors to keep more languages in scope and maintain their diversity commitments. Faster, higher-quality migration supports the ultimate goal: inclusive trials that reflect real-world patient populations.
AI is not a replacement for human expertise; it’s a quality multiplier. AI excels at repetitive, rules-based mapping and syntax validation; humans excel at judgment, nuance, and ensuring regulatory compliance.
The guiding principle is to automate where it improves consistency and apply human review where contextual understanding is critical. This hybrid approach, where AI migration is followed by human linguistic review, creates a faster, more accurate localization cycle while maintaining the integrity of patient-facing content.
This was an initial study with a limited sample size (n=15). While results were consistent across languages, larger-scale testing will help refine how AI performs further COA formats and languages.
Future research will explore integrating AI not only into migration but also into error detection and automated quality scoring, giving sponsors even greater transparency into localization readiness.
AI isn’t replacing linguists, it’s empowering them. By automating the error-prone migration step, we can deliver faster, cleaner, and more reliable localized content that accelerates global clinical trials.
The early data is clear: AI reduces human error, cuts re-proofing cycles, and preserves the linguistic integrity that regulatory bodies demand. For sponsors, it means faster submissions and global launches. For patients, it means access, no matter what language they speak. And for linguists, it means spending more time on the value-add work: refining meaning, context, and cultural accuracy, rather than manually moving text from one format to another.
The full ISPOR poster can be found at
Jonathan Norman, Director of Localization & Scale Management, YPrime
