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arXiv · 2407.18483

A Role-specific Guided Large Language Model for Ophthalmic Consultation Based on Stylistic Differentiation

Laiyi Fu·Binbin Fan·Hongkai Du·Yanxiang Feng·Chunhua Li·Huping Song
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Abstract

Ophthalmology consultations are crucial for diagnosing, treating, and preventing eye diseases. However, the growing demand for consultations exceeds the availability of ophthalmologists. By leveraging large pre-trained language models, we can design effective dialogues for specific scenarios, aiding in consultations. Traditional fine-tuning strategies for question-answering tasks are impractical due to increasing model size and often ignoring patient-doctor role function during consultations. In this paper, we propose EyeDoctor, an ophthalmic medical questioning large language model that enhances accuracy through doctor-patient role perception guided and an augmented knowledge base with external disease information. Experimental results show EyeDoctor achieves higher question-answering precision in ophthalmology consultations. Notably, EyeDoctor demonstrated a 7.25% improvement in Rouge-1 scores and a 10.16% improvement in F1 scores on multi-round datasets compared to second best model ChatGPT, highlighting the importance of doctor-patient role differentiation and dynamic knowledge base expansion for intelligent medical consultations. EyeDoc also serves as a free available web based service and souce code is available at https://github.com/sperfu/EyeDoc.

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Laiyi Fu, Binbin Fan, Hongkai Du, Yanxiang Feng, Chunhua Li, Huping Song. 2024-07-31. A Role-specific Guided Large Language Model for Ophthalmic Consultation Based on Stylistic Differentiation. https://arxiv.org/abs/2407.18483

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