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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
6/01/2025
Date last updated
6/01/2025

Titles & IDs
Public title
Study on the Medical Education Capability of the EyeTeacher Artificial Intelligence Platform
Scientific title
Study on the Medical Education Capability of the EyeTeacher Artificial Intelligence Platform
Secondary ID [1] 0 0
THU01-20240100
Universal Trial Number (UTN)
Trial acronym
EyeTeacher
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Medical Education 0 0
Condition category
Condition code

Intervention/exposure
Study type
Interventional
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).
Intervention code [1] 0 0
Other interventions
Comparator / control treatment
Control group

Outcomes
Primary outcome [1] 0 0
Grade of ophthalmology examination
Timepoint [1] 0 0
1 day after complete lectures
Secondary outcome [1] 0 0
Time of studying ophthalmology
Timepoint [1] 0 0
1 day after complete lectures
Secondary outcome [2] 0 0
Feedback of teaching from students
Timepoint [2] 0 0
1 day after complete lectures

Eligibility
Key inclusion criteria
* Clinical medical students who have not taken ophthalmology courses
* Age 21-40
* Gender not restricted
* Sign the informed consent form
Minimum age
21 Years
Maximum age
40 Years
Sex
Both males and females
Can healthy volunteers participate?
Yes
Key exclusion criteria
* Refusal of the research protocol.
* Participants unwilling or unable to understand and complete the questionnaire.

Study design
Purpose of the study
Other
Allocation to intervention
Randomised controlled trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
Masking / blinding
Blinded (masking used)
Who is / are masked / blinded?


The people assessing the outcomes
Intervention assignment
Parallel
Other design features
Phase
Not applicable
Type of endpoint/s
Statistical methods / analysis

Recruitment
Recruitment status
Not yet recruiting
Data analysis
Reason for early stopping/withdrawal
Other reasons
Date of first participant enrolment
Anticipated
5/01/2025
Actual
Date of last participant enrolment
Anticipated
Actual
Date of last data collection
Anticipated
30/12/2025
Actual
Sample size
Target
252
Accrual to date
Final
Recruitment in Australia
Recruitment state(s)
VIC
Recruitment hospital [1] 0 0
University of Melbourne - Melbourne
Recruitment postcode(s) [1] 0 0
3010 - Melbourne
Recruitment outside Australia
Country [1] 0 0
China
State/province [1] 0 0
Beijing
Country [2] 0 0
Ghana
State/province [2] 0 0
Greater Accra Region
Country [3] 0 0
India
State/province [3] 0 0
Tamil Nadu
Country [4] 0 0
Malaysia
State/province [4] 0 0
Kuala Lumpur
Country [5] 0 0
Singapore
State/province [5] 0 0
Singapore
Country [6] 0 0
United Kingdom
State/province [6] 0 0
London

Funding & Sponsors
Primary sponsor type
Other
Name
Tsinghua University
Address
Country

Ethics approval
Ethics application status

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.
Trial website
https://clinicaltrials.gov/study/NCT06759012
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.
Public notes

Contacts
Principal investigator
Name 0 0
Address 0 0
Country 0 0
Phone 0 0
Fax 0 0
Email 0 0
Contact person for public queries
Name 0 0
Yueyuan Xu
Address 0 0
Country 0 0
Phone 0 0
+86 186 1065 2799
Fax 0 0
Email 0 0
[email protected]
Contact person for scientific queries



Summary Results

For IPD and results data, please see https://clinicaltrials.gov/study/NCT06759012