A large language model digital patient system enhances ophthalmology history taking skills
Abstract Clinical trainees face limited opportunities to practice medical history-taking skills due to scarce case diversity and access to real patients. To address this, we developed a large language model-based digital patient (LLMDP) system that transforms de‑identified electronic health records...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01841-6 |
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| author | Ming-Jie Luo Shaowei Bi Jianyu Pang Lixue Liu Ching-Kit Tsui Yunxi Lai Wenben Chen Yahan Yang Kezheng Xu Lanqin Zhao Ling Jin Duoru Lin Xiaohang Wu Jingjing Chen Rongxin Chen Zhenzhen Liu Yuxian Zou Yangfan Yang Yiqing Li Haotian Lin |
| author_facet | Ming-Jie Luo Shaowei Bi Jianyu Pang Lixue Liu Ching-Kit Tsui Yunxi Lai Wenben Chen Yahan Yang Kezheng Xu Lanqin Zhao Ling Jin Duoru Lin Xiaohang Wu Jingjing Chen Rongxin Chen Zhenzhen Liu Yuxian Zou Yangfan Yang Yiqing Li Haotian Lin |
| author_sort | Ming-Jie Luo |
| collection | DOAJ |
| description | Abstract Clinical trainees face limited opportunities to practice medical history-taking skills due to scarce case diversity and access to real patients. To address this, we developed a large language model-based digital patient (LLMDP) system that transforms de‑identified electronic health records into voice‑enabled virtual patients capable of free‑text dialog and adaptive feedback, based on our previously established open-source retrieval-augmented framework. In a single‑center randomized controlled trial (ClinicalTrials.gov: NCT06229379; N = 84), students trained with LLMDP achieved a 10.50-point increase in medical history-taking assessment scores (95% CI: 4.66–16.33, p < 0.001) compared to those using traditional methods. LLMDP-trained students also demonstrated greater empathy. Participants reported high satisfaction with LLMDP, emphasizing its role in reducing training costs and boosting confidence for real patient interactions. These findings provide evidence that LLM-driven digital patients enhance medical history-taking skills and offer a scalable, low-risk pathway for integrating generative AI into ophthalmology education. |
| format | Article |
| id | doaj-art-2226b4249747426eb8da3acdc4f95c8d |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-2226b4249747426eb8da3acdc4f95c8d2025-08-20T04:02:46ZengNature Portfolionpj Digital Medicine2398-63522025-08-018111210.1038/s41746-025-01841-6A large language model digital patient system enhances ophthalmology history taking skillsMing-Jie Luo0Shaowei Bi1Jianyu Pang2Lixue Liu3Ching-Kit Tsui4Yunxi Lai5Wenben Chen6Yahan Yang7Kezheng Xu8Lanqin Zhao9Ling Jin10Duoru Lin11Xiaohang Wu12Jingjing Chen13Rongxin Chen14Zhenzhen Liu15Yuxian Zou16Yangfan Yang17Yiqing Li18Haotian Lin19State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesAbstract Clinical trainees face limited opportunities to practice medical history-taking skills due to scarce case diversity and access to real patients. To address this, we developed a large language model-based digital patient (LLMDP) system that transforms de‑identified electronic health records into voice‑enabled virtual patients capable of free‑text dialog and adaptive feedback, based on our previously established open-source retrieval-augmented framework. In a single‑center randomized controlled trial (ClinicalTrials.gov: NCT06229379; N = 84), students trained with LLMDP achieved a 10.50-point increase in medical history-taking assessment scores (95% CI: 4.66–16.33, p < 0.001) compared to those using traditional methods. LLMDP-trained students also demonstrated greater empathy. Participants reported high satisfaction with LLMDP, emphasizing its role in reducing training costs and boosting confidence for real patient interactions. These findings provide evidence that LLM-driven digital patients enhance medical history-taking skills and offer a scalable, low-risk pathway for integrating generative AI into ophthalmology education.https://doi.org/10.1038/s41746-025-01841-6 |
| spellingShingle | Ming-Jie Luo Shaowei Bi Jianyu Pang Lixue Liu Ching-Kit Tsui Yunxi Lai Wenben Chen Yahan Yang Kezheng Xu Lanqin Zhao Ling Jin Duoru Lin Xiaohang Wu Jingjing Chen Rongxin Chen Zhenzhen Liu Yuxian Zou Yangfan Yang Yiqing Li Haotian Lin A large language model digital patient system enhances ophthalmology history taking skills npj Digital Medicine |
| title | A large language model digital patient system enhances ophthalmology history taking skills |
| title_full | A large language model digital patient system enhances ophthalmology history taking skills |
| title_fullStr | A large language model digital patient system enhances ophthalmology history taking skills |
| title_full_unstemmed | A large language model digital patient system enhances ophthalmology history taking skills |
| title_short | A large language model digital patient system enhances ophthalmology history taking skills |
| title_sort | large language model digital patient system enhances ophthalmology history taking skills |
| url | https://doi.org/10.1038/s41746-025-01841-6 |
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