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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model.

Aidan Gilson¹, Xuguang Ai², Qianqian Xie²

¹Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.

|October 1, 2025

Summary

This summary is machine-generated.

A new ophthalmology-specific Large Language Model (LLM), Language Enhanced Model for Eye (LEME), shows superior performance in various clinical tasks. This open-source LLM has the potential to advance healthcare and research collaboration in ophthalmology.

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Area of Science:

Artificial Intelligence in Medicine
Ophthalmology
Natural Language Processing

Background:

Large Language Models (LLMs) are transforming healthcare, but specialized ophthalmology LLMs are scarce.
Existing LLMs lack the specific training for complex ophthalmological tasks.

Purpose of the Study:

To introduce and evaluate Language Enhanced Model for Eye (LEME), an open-source LLM tailored for ophthalmology.
To benchmark LEME against other leading LLMs in ophthalmology-specific tasks.

Main Methods:

LEME was pre-trained on Llama2 70B and fine-tuned with ~127,000 ophthalmology-specific training instances.
Evaluations included internal (abstract completion, fill-in-the-blank, MCQ, short-answer QA) and external (long-form QA, EHR summarization, clinical QA) tasks.
LEME was benchmarked against GPT-3.5, GPT-4, Llama2 models, PMC-LLAMA 13B, Meditron 70B, and EYE-Llama.

Main Results:

LEME outperformed other LLMs in internal tasks, including abstract completion (Rouge-L 0.20 ± 0.03) and fill-in-the-blank (Rouge-L 0.82 ± 0.04).
In external validations, LEME achieved high scores in long-form QA (Rouge-L 0.19 ± 0.01) and ranked highest in EHR summarization and clinical QA.
LEME demonstrated strong performance in MCQ accuracy (0.68 ± 0.09), ranking second overall.

Conclusions:

LEME represents a significant advancement in open-source ophthalmology-specific LLMs.
Its robust fine-tuning and use of specialized data position it to revolutionize clinical tasks and research.
LEME has the potential to democratize research collaboration and improve healthcare outcomes in ophthalmology.