A Role-specific Guided Large Language Model for Ophthalmic Consultation Based on Stylistic Differentiation
<|reference_start|>A Role-specific Guided Large Language Model for Ophthalmic Consultation Based on Stylistic Differentiation: 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.<|reference_end|>
@article{fu2024a,
    title={A Role-specific Guided Large Language Model for Ophthalmic Consultation
  Based on Stylistic Differentiation},
    author={Laiyi Fu, Binbin Fan, Hongkai Du, Yanxiang Feng, Chunhua Li, Huping
  Song},
    journal={arXiv preprint arXiv:2407.18483},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18483},
    primaryClass={cs.CL cs.AI}
}