The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine
10.1016/j.dcmed.2024.09.001
- Author:
GAO Yuan
1
,
2
,
3
;
WU Zixuan
1
;
SHENG Boyang
1
;
ZHANG Fu
3
,
4
;
CHENG Yong
3
,
4
;
YAN Junfeng
5
;
PENG Qinghua
1
Author Information
1. School of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
2. Department of Preventive Treatment of Disease, People&rsquo
3. s Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350004, China
4. Department of Preventive Treatment of Disease, People&rsquo
5. School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
- Publication Type:Review
- Keywords:
Traditional Chinese medicine (TCM);
Eye diagnosis;
Artificial intelligence (AI);
Large-scale model;
Self-supervised learning;
Deep neural network
- From:
Digital Chinese Medicine
2024;7(2):101-107
- CountryChina
- Language:English
-
Abstract:
Abstract:Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the
eyes. With the development of intelligent diagnosis in traditional Chinese medicine (TCM), artificial intelligence (AI) can improve the accuracy and efficiency of eye diagnosis. However, the research on intelligent eye diagnosis still faces many challenges, including the lack of standardized and precisely labeled data, multi-modal information analysis, and artificial intelligence models for syndrome differentiation. The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelligence. This study elaborates on the three key technologies of AI models in the intelligent application of TCM eye diagnosis, and explores the implications for the research of eye diagnosis intelligence. First, a database concerning eye diagnosis was established based on self-supervised learning so as to solve the issues related to the lack of standardized and precisely labeled data. Next, the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis. Last, the building of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome differentiation models. In summary, research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.
- Full text:2024100815213064849gaoyuan.docx
