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Trial details imported from ClinicalTrials.gov
For full trial details, please see the original record at
https://clinicaltrials.gov/study/NCT06759012
Registration number
NCT06759012
Ethics application status
Date submitted
5/12/2024
Date registered
25/03/2025
Date last updated
25/03/2025
Titles & IDs
Public title
Study on the Medical Education Capability of the EyeTeacher Artificial Intelligence Platform
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Scientific title
Study on the Medical Education Capability of the EyeTeacher Artificial Intelligence Platform
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Secondary ID [1]
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THU01-20240100
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Universal Trial Number (UTN)
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Trial acronym
EyeTeacher
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Linked study record
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Health condition
Health condition(s) or problem(s) studied:
Medical Education
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Condition category
Condition code
Intervention/exposure
Study type
Interventional
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Description of intervention(s) / exposure
Other interventions - EyeTeacher
Experimental: Interventional group - Attend EyeTeacher lecture before taking the regular ophthalmology lecture in medical school.
No intervention: Control group - Attend regular ophthalmology lectures in medical school.
Other interventions: EyeTeacher
Participants randomized to the intervention group will receive access to the EyeTeacher system, along with their username, password, and a user manual, one week before the start of the course. They will follow instructions on the website and complete a quiz before and after each course. After completing the EyeTeacher curriculum, they will take the first examination (Examination 1) and complete a set of questionnaires. A classroom will be provided for study purposes, but no restrictions will be imposed on the study location. Following the EyeTeacher section, participants will attend the ophthalmology course in the regular training program, with support from the EyeTeacher system. They will be evaluated according to the ophthalmology posting's evaluation criteria (Examination 2).
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Intervention code [1]
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Other interventions
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Comparator / control treatment
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Control group
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Outcomes
Primary outcome [1]
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Grade of ophthalmology examination
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Assessment method [1]
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Title: Ophthalmology Theoretical Examination. The examination includes 40 multiple-choice questions and two essay questions related to ophthalmology knowledge, to be completed within one hour without the use of reference materials. The exam will be graded by experts based on a standardized answer key. The score ranges from 0 to 100, with a higher score indicating a deeper understanding of the ophthalmology knowledge. Students in both the interventional group and the control group will complete the exam within the specified time on the EyeTeacher system. The data will be collected in the system's backend.
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Timepoint [1]
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1 day after complete lectures
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Secondary outcome [1]
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Time of studying ophthalmology
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Assessment method [1]
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The study time spent using each teaching module in the interventional group is tracked by the EyeTeacher system website, while the study time for the control group and the out-of-system study time for the interventional group are collected through a questionnaire. Questionnaire title: Time of Study in Ophthalmology The questionnaire consists of fill-in-the-blank questions, with time measured in minutes. The content includes: class time, exercise time, discussion time, and reading reference material time. The questionnaires for both the interventional and control groups are completed on the EyeTeacher system, and the data is collected through the backend.
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Timepoint [1]
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1 day after complete lectures
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Secondary outcome [2]
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Feedback of teaching from students
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Assessment method [2]
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Questionnaire title: Feedback of Study in Ophthalmology: The Likert scale is used for scoring. The topics include students' evaluations of course design, teacher guidance, empathy, as well as students' self-assessment and evaluation of their interest in ophthalmology. The questionnaires for both the interventional and control groups are completed on the EyeTeacher system, and the data is collected through the backend.
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Timepoint [2]
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1 day after complete lectures
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Eligibility
Key inclusion criteria
* Clinical medical students who have not taken ophthalmology courses
* Age 21-40
* Gender not restricted
* Sign the informed consent form
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Minimum age
21
Years
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Maximum age
40
Years
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Sex
Both males and females
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Can healthy volunteers participate?
Yes
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Key exclusion criteria
* Refusal of the research protocol.
* Participants unwilling or unable to understand and complete the questionnaire.
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Study design
Purpose of the study
Other
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Allocation to intervention
Randomised controlled trial
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Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
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Methods used to generate the sequence in which subjects will be randomised (sequence generation)
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Masking / blinding
Blinded (masking used)
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Who is / are masked / blinded?
The people assessing the outcomes
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Intervention assignment
Parallel
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Other design features
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Phase
Not applicable
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Type of endpoint/s
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Statistical methods / analysis
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Recruitment
Recruitment status
Not yet recruiting
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Data analysis
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Reason for early stopping/withdrawal
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Other reasons
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Date of first participant enrolment
Anticipated
5/01/2025
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Actual
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Date of last participant enrolment
Anticipated
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Actual
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Date of last data collection
Anticipated
30/12/2025
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Actual
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Sample size
Target
252
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Accrual to date
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Final
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Recruitment in Australia
Recruitment state(s)
VIC
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Recruitment hospital [1]
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University of Melbourne - Melbourne
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Recruitment postcode(s) [1]
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3010 - Melbourne
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Recruitment outside Australia
Country [1]
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China
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State/province [1]
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Beijing
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Country [2]
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Ghana
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State/province [2]
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Greater Accra Region
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Country [3]
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India
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State/province [3]
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Tamil Nadu
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Country [4]
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Malaysia
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State/province [4]
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Kuala Lumpur
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Country [5]
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Singapore
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State/province [5]
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Singapore
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Country [6]
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United Kingdom
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State/province [6]
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London
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Funding & Sponsors
Primary sponsor type
Other
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Name
Tsinghua University
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Address
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Country
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Ethics approval
Ethics application status
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Summary
Brief summary
With the rise of generative artificial intelligence and large language models, medical education is undergoing a significant transformation. Numerous studies have highlighted the limitations of traditional educational knowledge acquisition and the potential impact of artificial intelligence on medical education, resident training, and continuing education for clinical practitioners. However, there is a lack of real-world experiments on the effectiveness of AI-integrated education. Artificial intelligence can provide extensive educational resources and tools that are not limited by geographical location or language, thereby lowering the barrier to accessing high-quality medical education and promoting educational equity. Nevertheless, the performance of AI models trained by different medical institutions or healthcare systems may vary. To offer a more universal, accessible, high-quality, and interconnected educational journey. We have developed a virtual ophthalmology teacher, which developed based on foundational model and large language models. This model, named EyeTeacher provide comprehensive theoretical knowledge and clinical skills enhancement for untrained medical students. To verify the effectiveness of our EyeTeacher across different national ophthalmology education systems and languages, we plan to implement a randomized controlled trial. This trial will assess the clinical capabilities of all participants and explore the advantages and disadvantages of the system compared to traditional teaching methods.
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Trial website
https://clinicaltrials.gov/study/NCT06759012
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Trial related presentations / publications
He B, Kwan AC, Cho JH, Yuan N, Pollick C, Shiota T, Ebinger J, Bello NA, Wei J, Josan K, Duffy G, Jujjavarapu M, Siegel R, Cheng S, Zou JY, Ouyang D. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature. 2023 Apr;616(7957):520-524. doi: 10.1038/s41586-023-05947-3. Epub 2023 Apr 5. Benitez TM, Xu Y, Boudreau JD, Kow AWC, Bello F, Van Phuoc L, Wang X, Sun X, Leung GK, Lan Y, Wang Y, Cheng D, Tham YC, Wong TY, Chung KC. Harnessing the potential of large language models in medical education: promise and pitfalls. J Am Med Inform Assoc. 2024 Feb 16;31(3):776-783. doi: 10.1093/jamia/ocad252.
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Public notes
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Contacts
Principal investigator
Name
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Address
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Country
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Phone
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Fax
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Email
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Contact person for public queries
Name
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Yueyuan Xu
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Address
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Country
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Phone
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+86 186 1065 2799
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Fax
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Email
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xuyy17@mails.tsinghua.edu.cn
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Contact person for scientific queries
Data sharing statement
What supporting documents are/will be available?
No Supporting Document Provided
Results publications and other study-related documents
No documents have been uploaded by study researchers.
Results not provided in
https://clinicaltrials.gov/study/NCT06759012
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