An interventional study of "Digital twin patient" and Interaction with real patients in Cataract, Glaucoma and Diabetic Retinopathy, sponsored by Sun Yat-sen University. Completed at 1 site in China. Open to participants aged 18 Years to 25 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2024-11-22.
Sponsored by Sun Yat-sen University · Not applicable, Interventional, and Other
Among 52 studies in Cataract by Sun Yat-sen University — 3 open to participants now, 4 under way, none of them phase 3. Cataract · Sun Yat-sen University.
Among the 52 studies in Cataract by Sun Yat-sen University, this one is the 18th largest by enrollment of the 35 interventional ones, is one of 4 that started in 2023, is one of 35 with no phase, and was the 52nd registered.
1,629 studies on the registry are indexed under Cataract; 235 are open to participants now.
This study's enrollment of 84 is close to the median of 79 across 1,092 interventional studies indexed under Cataract.
Browse Cataract studies →3 of the 52 studies in Cataract by Sun Yat-sen University are open now, in 1 country. Each count lists the studies.
Of the 141 Cataract studies recruiting now, 88 (62%) set no upper age limit and 8 (6%) stop at 75 or younger; 10 take children under 18; 16 take healthy volunteers.
This study: 18 Years to 25 Years · all sexes · healthy volunteers too.
The full table on the hub →52 studies; the count open now beside each. This study's row is marked.
First in 2005; this study’s year is marked. 4 started in 2023.
Median enrollment 84 across the 35 interventional studies, 428 across the 17 observational. Of the 32 that completed or were terminated, 2 (6%) have results posted.
Sun Yat-sen University is the lead sponsor of 1,690 studies on the registry; 619 are open to participants now.
None of the studies under way reaches primary completion within a year.
Among the 52 studies in Cataract by Sun Yat-sen University, from their registry updates:
Nothing recorded among them since 2026-09-25.
The researchers have used the ophthalmology textbook, clinical guideline consensus, the Internet conversation data and knowledge base of Zhongshan Ophthalmology Center in the early stage, combined with artificial feedback reinforcement learning and other techniques to fine-tune and train the LLM, and developed "Digital Twin Patient", a localized large language model that has the ability to answer ophthalmology-related medical questions, and also constructed a combination of automated model evaluation and manual evaluation by medical experts. The evaluation system combining automated model evaluation and manual evaluation by medical experts was constructed at the same time.
This project intends to integrate "Digital Twin Patient" into undergraduate ophthalmology apprenticeship, simulate the consultation process of real patients through the online interaction between students and "Digital Twin Patient", explore the effect of "Digital Twin Patient" consultation teaching, provide emerging technology tools for guiding medical students to actively learn a variety of ophthalmology cases, cultivate clinical thinking, and provide the possibility of creating a new mode of intelligent teaching.
At present, the main form of clinical questioning skills teaching is to let undergraduates who participate in the apprenticeship first learn the characteristics and diagnostic points of cases, and then practice questioning on real patients in the wards. However, due to the large number of trainee students, it is difficult to meet the teaching demand in terms of the number of cases available for questioning and the richness of disease types under the current teaching mode. Therefore, it is necessary to utilize new intelligent technologies and create a new model of questioning skills teaching to improve teaching efficiency and enhance students' clinical thinking.
Large-scale language modeling (LLM) is a deep learning technology that can learn knowledge from a large amount of text, and AI chatbots such as ChatGPT are a typical example of its application. AI chatbots are characterized by anthropomorphic comprehension and diversified natural language generation abilities in different contexts, and have been initially applied in the medical field, such as passing the U.S. Medical Licensing Examination, assisting in ophthalmic history documentation and answering ophthalmic questions. However, it has been found that although LLM has fair modeling performance in general medical knowledge, it still needs to be improved in the area of specialty diseases. Based on this, the researcher's team has used the ophthalmology textbook, clinical guideline consensus, the Internet conversation data and knowledge base of Zhongshan Ophthalmology Center in the early stage, combined with artificial feedback reinforcement learning and other techniques to fine-tune and train the LLM, and developed "Digital Twin Patient", a localized large language model that has the ability to answer ophthalmology-related medical questions, and also constructed a combination of automated model evaluation and manual evaluation by medical experts. The evaluation system combining automated model evaluation and manual evaluation by medical experts was constructed at the same time.
This project intends to integrate "Digital Twin Patient" into undergraduate ophthalmology apprenticeship, simulate the consultation process of real patients through the online interaction between students and "Digital Twin Patient", explore the effect of "Digital Twin Patient" consultation teaching, provide emerging technology tools for guiding medical students to actively learn a variety of ophthalmology cases, cultivate clinical thinking, and provide the possibility of creating a new mode of intelligent teaching.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
The students in the "Digital twin patient" group were trained in history taking using a "digital twin patient" on the first day of a training program, and then took a 15-minute clinical questioning exam using the "digital twin patient" on the second day.
Device: "Digital twin patient"
The students in the Real patient group were trained in history taking using a real patient on the first day of a training program, and then took a 15-minute clinical questioning exam using the "digital twin patient" on the second day.
Device: "Digital twin patient" · Behavioral: Interaction with real patients
"Digital twin patient" can serve as patients with specific diseases for medical students to acquire disease history and thus practice clinical questioning skills.
As in traditional medical education, medical students need to interact with real patients to practice history collection skills.
Students' scores in the medical history acquisition exam
Time frame: Weekly during this study (up to 10 months)
Plan to share: No
No publications or documents are linked to this record.
Ran from Nov 2023 to primary completion in May 2024, 5 months.
Of 300 non-industry interventional studies with no phase under Cataract, half reached primary completion within 18 months of starting (the median), counting those still running; a quarter within 7 and three quarters within 30. This study's 5 months is below that.
Counting every sponsor, half of 490 studies reached it within 17 months; a quarter within 9 and three quarters within 28.
How these figures are worked out, and which studies are left out of them.
This study is completed, as verified in Feb 2024. You cannot join it, but the record below documents what was studied.
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