Introduction: Gestational Dry Eye Disease (DED) affects up to 50% of expectant mothers, yet current diagnostic tools are generic and fail to capture pregnancy-specific symptom patterns. Developing and validating new instruments in this population is logistically and ethically challenging due to recruitment barriers. This study describes the development and computational prototyping of the Dry Eye Disease in Pregnancy Questionnaire (DED-PREG) using a Generative Artificial Intelligence (GenAI) framework. Methods: We utilized a multi-stage in silico framework involving two independent synthetic cohorts. First, a qualitative focus group cohort was generated to simulate clinical dialogues for content derivation, followed by semantic vectorization for algorithmic item reduction. Subsequently, an independent validation cohort of 500 pregnant personas was instantiated. We evaluated the resulting 20-item instrument for internal consistency, structural validity, and test-retest reliability via a longitudinal simulation engine utilizing temporal context injection to model gestational progression across five distinct timepoints (T1–T5). Results: The DED-PREG mapped to three distinct domains: Ocular Symptoms, Functional Impact, and Lifestyle RMSEA = 0.11). Longitudinal analysis confirmed the instrument’s responsiveness to gestational change (Global Cohen’s d = 0.44), with Linear Mixed Models (LMM) revealing a significant interaction between low socioeconomic status and symptom exacerbation (β=0.053, p < 0.001). Conclusion: This study presents the first pregnancy-specific DED instrument structurally optimized via AI simulation. While human validation remains the gold standard, this computational approach demonstrates that GenAI can serve as a rigorous “stress-test” for instrument design, enabling the rapid prototyping of robust clinical tools prior to in vivo deployment. Plain Language Summary: Dry Eye Disease affects nearly 50% of pregnant women, but current diagnostic tools fail to capture specific pregnancy-related symptoms. Developing new tests is difficult due to the logistical and ethical barriers in recruiting expectant mothers for research. This study used Generative AI to design and validate a new questionnaire, called DED-PREG. By testing the tool on 500 “digital personas” simulating pregnancy, researchers found the survey was accurate, reliable, and effective at tracking symptom changes across trimesters. This approach proves AI can rigorously “stress-test” medical instruments, enabling the rapid creation of better clinical tools before testing on real patients. Keywords: dry eye disease, PROM, large language models
Jaruchowska et al. (Sun,) studied this question.