Clinical trial NCT06759012: Study on the Medical Education Capability of the EyeTeacher Artificial Intelligence Platform
Official title: Study on the Medical Education Capability of the EyeTeacher Artificial Intelligence Platform
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Medical Education
Keywords: Medical education
Multimodality AI
Generative AI
Lead sponsor: Tsinghua University
Sponsor class: OTHER
First posted: 2025-01-06
Enrollment: 252
Sex: ALL
Minimum age (years): 21
Maximum age (years): 40
Standard ages: ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: OTHER

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.


Eligibility criteria: Inclusion Criteria:

* Clinical medical students who have not taken ophthalmology courses
* Age 21-40
* Gender not restricted
* Sign the informed consent form

Exclusion Criteria:

* Refusal of the research protocol.
* Participants unwilling or unable to understand and complete the questionnaire.

Primary outcomes:
measure: Grade of ophthalmology examination
timeFrame: 1 day after complete lectures
Secondary outcomes:
measure: Time of studying ophthalmology
timeFrame: 1 day after complete lectures
measure: Feedback of teaching from students
timeFrame: 1 day after complete lectures
Other outcomes:
Locations:
city: Melbourne
country: Australia
facility: University of Melbourne
state: Victoria
city: Beijing
country: China
facility: Peking Union Medical College Hospital
state: Beijing
city: Accra
country: Ghana
facility: University of Ghana
state: Greater Accra Region
city: Chennai
country: India
facility: Sankara Nethralaya
state: Tamil Nadu
city: Kuala Lumpur
country: Malaysia
facility: University of Malaya
city: Singapore
country: Singapore
facility: Duke-NUS medical school
city: London
country: United Kingdom
facility: Moorfields Eye Hospital
Study officials:
References:
citation: 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.
citation: 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.