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Using Large Language Models for In Silico Development and Simulation of a Patient-Reported Outcome Questionnaire for

Ewelina Trojacka1, Joanna Przybek-Skrzypecka2,3, Justyna Izdebska1,2,3

  • 1Center of Ocular Microsurgery, Professor Jerzy Szaflik's Clinic in Warsaw, 00-215 Warszawa, Poland.

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|January 10, 2026
PubMed
Summary
This summary is machine-generated.

Large Language Models (LLMs) enable in silico pre-validation of ophthalmology Patient-Reported Outcome Measures (PROMs), ensuring robust and unbiased instruments before clinical trials by simulating patient responses.

Keywords:
GenAILLMs-assisted PROMPROMcataract surgeryvisual symptoms questionnaire

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Psychometrics

Background:

  • Patient-Reported Outcome Measures (PROMs) in ophthalmology face challenges with high patient burden during early validation.
  • In silico pre-validation using Large Language Models (LLMs) offers a novel approach to streamline instrument development.

Purpose of the Study:

  • To develop and evaluate an In Silico Pre-validation Framework utilizing LLMs for ophthalmology PROMs.
  • To assess the framework's ability to stress-test instruments and ensure robustness before clinical deployment.

Main Methods:

  • An LLM generated a PROM questionnaire and a synthetic cohort of 500 patient profiles using a Python pipeline.
  • Patient profiles included detailed demographics, lifestyle, health background, and clinical parameters (e.g., IOL type, dysphotopsia severity).
  • Psychometric validation involved Confirmatory Factor Analysis (CFA) and Differential Item Functioning (DIF) analysis with a stateless simulation for test-retest reliability.

Main Results:

  • The LLM-based framework demonstrated excellent structural validity (CFI=0.962, TLI=0.951, RMSEA=0.048, SRMR=0.063).
  • No significant bias was detected by DIF analysis across age, sex, or IOL type.
  • High internal consistency (Cronbach's alpha > 0.80), test-retest reliability (ICC > 0.90), and convergent validity with NEI-VFQ-25 were observed.

Conclusions:

  • LLM-based pre-validation achieves "algorithmic fidelity," effectively mirroring complex human response patterns.
  • This in silico framework identifies structural weaknesses in PROMs prior to clinical trials.
  • The approach reduces the ethical and logistical burden associated with traditional PROM validation in real-world populations.