VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledge
<|reference_start|>VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledge: The need for improved diagnostic methods in ophthalmology is acute, especially in the less developed regions with limited access to specialists and advanced equipment. Therefore, we introduce VisionUnite, a novel vision-language foundation model for ophthalmology enhanced with clinical knowledge. VisionUnite has been pretrained on an extensive dataset comprising 1.24 million image-text pairs, and further refined using our proposed MMFundus dataset, which includes 296,379 high-quality fundus image-text pairs and 889,137 simulated doctor-patient dialogue instances. Our experiments indicate that VisionUnite outperforms existing generative foundation models such as GPT-4V and Gemini Pro. It also demonstrates diagnostic capabilities comparable to junior ophthalmologists. VisionUnite performs well in various clinical scenarios including open-ended multi-disease diagnosis, clinical explanation, and patient interaction, making it a highly versatile tool for initial ophthalmic disease screening. VisionUnite can also serve as an educational aid for junior ophthalmologists, accelerating their acquisition of knowledge regarding both common and rare ophthalmic conditions. VisionUnite represents a significant advancement in ophthalmology, with broad implications for diagnostics, medical education, and understanding of disease mechanisms.<|reference_end|>
@article{li2024visionunite:,
    title={VisionUnite: A Vision-Language Foundation Model for Ophthalmology
  Enhanced with Clinical Knowledge},
    author={Zihan Li, Diping Song, Zefeng Yang, Deming Wang, Fei Li, Xiulan Zhang,
  Paul E. Kinahan, Yu Qiao},
    journal={arXiv preprint arXiv:2408.02865},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02865},
    primaryClass={eess.IV cs.AI cs.CL cs.CV}
}