| Title: | Integrated image-based deep learning and language models for primary diabetes care |
| Journal: | Nature Medicine |
| Published: | 19 Jul 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39030266/ |
| DOI: | https://doi.org/10.1038/s41591-024-03139-8 |
Publication 12800
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Abstract
Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.</p>
93 Authors
- Jiajia Li
- Zhouyu Guan
- Jing Wang
- Carol Y. Cheung
- Yingfeng Zheng
- Lee-Ling Lim
- Cynthia Ciwei Lim
- Paisan Ruamviboonsuk
- Rajiv Raman
- Leonor Corsino
- Justin B. Echouffo-Tcheugui
- Andrea O. Y. Luk
- Li Jia Chen
- Xiaodong Sun
- Haslina Hamzah
- Qiang Wu
- Xiangning Wang
- Ruhan Liu
- Ya Xing Wang
- Tingli Chen
- Xiao Zhang
- Xiaolong Yang
- Jun Yin
- Jing Wan
- Wei Du
- Ten Cheer Quek
- Jocelyn Hui Lin Goh
- Dawei Yang
- Xiaoyan Hu
- Truong X. Nguyen
- Simon K. H. Szeto
- Peranut Chotcomwongse
- Rachid Malek
- Nargiza Normatova
- Nilufar Ibragimova
- Ramyaa Srinivasan
- Pingting Zhong
- Wenyong Huang
- Chenxin Deng
- Lei Ruan
- Cuntai Zhang
- Chenxi Zhang
- Yan Zhou
- Chan Wu
- Rongping Dai
- Sky Wei Chee Koh
- Adina Abdullah
- Nicholas Ken Yoong Hee
- Hong Chang Tan
- Zhong Hong Liew
- Carolyn Shan-Yeu Tien
- Shih Ling Kao
- Amanda Yuan Ling Lim
- Shao Feng Mok
- Lina Sun
- Jing Gu
- Liang Wu
- Tingyao Li
- Di Cheng
- Zheyuan Wang
- Yiming Qin
- Ling Dai
- Ziyao Meng
- Jia Shu
- Yuwei Lu
- Nan Jiang
- Tingting Hu
- Shan Huang
- Gengyou Huang
- Shujie Yu
- Dan Liu
- Weizhi Ma
- Minyi Guo
- Xinping Guan
- Xiaokang Yang
- Covadonga Bascaran
- Charles R. Cleland
- Yuqian Bao
- Elif I. Ekinci
- Alicia Jenkins
- Juliana C. N. Chan
- Yong Mong Bee
- Sobha Sivaprasad
- Jonathan E. Shaw
- Rafael Simó
- Pearse A. Keane
- Ching-Yu Cheng
- Gavin Siew Wei Tan
- Weiping Jia
- Yih-Chung Tham
- Huating Li
- Bin Sheng
- Tien Yin Wong
1 Application
| Application ID | Title |
|---|---|
| 104443 | The artificial intelligence supported system in the diagnosis and risk prediction of metabolic disorders based on multimodal data |
Enabling scientific discoveries that improve human health