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

• URL: http://arxiv.org/abs/2407.18483v4
• Date: Wed, 31 Jul 2024 07:24:30 GMT
• Title: A Role-specific Guided Large Language Model for Ophthalmic Consultation Based on Stylistic Differentiation
• Authors: Laiyi Fu, Binbin Fan, Hongkai Du, Yanxiang Feng, Chunhua Li, Huping Song,
• Abstract summary: We propose EyeDoctor, an ophthalmic medical questioning large language model. Experimental results show EyeDoctor achieves higher question-answering precision in ophthalmology consultations.
• Score: 2.0671213754662343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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