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Related Experiment Video

Updated: Sep 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Large language models provide discordant information compared to ophthalmology guidelines.

Andrea Taloni1,2,3,4, Antonia Carmen Sangregorio4, Giuseppe Alessio4

  • 1Department of Translational Medicine, University of Ferrara, Ferrara, Italy.

Scientific Reports
|July 2, 2025
PubMed
Summary

Large language models (LLMs) show high agreement with ophthalmology guidelines, but discordant answers may risk patient harm. Further evaluation is needed for safe clinical integration of AI in medicine.

Keywords:
AAOAmerican Academy of OphthalmologyArtificial intelligenceGuidelinesLarge language modelPreferred practice patterns

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • The American Academy of Ophthalmology (AAO) develops Preferred Practice Patterns (PPP) guidelines for ophthalmology.
  • Large language models (LLMs) are increasingly used in healthcare, necessitating evaluation of their adherence to clinical guidelines.

Purpose of the Study:

  • To assess the concordance of responses from five leading LLMs with AAO PPP guidelines.
  • To identify potential risks associated with LLM-generated ophthalmology information.

Main Methods:

  • Five LLMs (GPT-4o, GPT-o1, Claude 3.5 Sonnet, Gemini 1.5 Pro, DeepSeek-R1-Lite-Preview) were queried using open-ended questions based on AAO PPP.
  • Three blinded investigators evaluated LLM responses for concordance with guidelines.
  • Harm potential of discordant answers was assessed, and article referencing was noted.

Main Results:

  • Concordance rates varied, with Claude 3.5 Sonnet (92.5%) and GPT-o1 (91.8%) showing the highest agreement.
  • Gemini 1.5 Pro and DeepSeek-R1-Lite-Preview exhibited higher rates of potentially harmful discordant answers.
  • Gemini 1.5 Pro demonstrated superior transparency through a higher number of cited references.

Conclusions:

  • While LLMs generally align with ophthalmology guidelines, discordant responses pose a risk of patient harm.
  • Potential harms include delayed diagnosis and suboptimal treatment recommendations.
  • Continued research and careful implementation are crucial for safe AI integration in clinical ophthalmology.