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

TL;DR

This paper introduces EyeDoctor, a role-specific large language model for ophthalmic consultations that improves question-answering accuracy by incorporating doctor-patient role perception and external knowledge, outperforming existing models.

Contribution

The paper presents EyeDoctor, a novel ophthalmic LLM that leverages role differentiation and dynamic knowledge base expansion, addressing limitations of traditional fine-tuning methods.

Findings

EyeDoctor achieved 7.25% higher Rouge-1 scores.

EyeDoctor showed a 10.16% improvement in F1 scores.

The model outperformed ChatGPT on multi-round ophthalmic datasets.

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.