Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06229379 |
| Other study ID # |
2023KYPJ283 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
September 1, 2023 |
| Est. completion date |
June 2024 |
Study information
| Verified date |
January 2024 |
| Source |
Sun Yat-sen University |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
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.
Description:
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.
