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Performance of Large Language Models for Structured Recognition and Refractive Prediction

NCT07183891 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 100

Last updated 2025-09-19

No results posted yet for this study

Summary

We conducted a single-center, retrospective observational study to evaluate large language models (ChatGPT 4o, GPT-5, DeepSeek) for automated interpretation of de-identified IOLMaster 700 reports provided as raster images. Models produced structured biometric extraction, toric IOL recommendation, and refractive predictions (sphere, cylinder, axis). Primary outcomes included parameter-level agreement and refractive error metrics; secondary outcomes included decision-support performance for toric IOL selection and agreement on ordered T-codes. No clinical intervention was performed.

Conditions

  • Cataract

Sponsors & Collaborators

  • Jin Yang

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-08-01
Primary Completion
2030-12-31
Completion
2035-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07183891 on ClinicalTrials.gov