The Effects of a Large Language Model on Clinical Questioning Skills

Sun Yat-sen University

Status: Completed

Conditions: Conjunctivitis; Cataract; Glaucoma; Keratitis; Diabetic Retinopathy

Treatments: Behavioral: Interaction with real patients; Device: "Digital twin patient"

Study type: Interventional

Funder types: Other

Identifiers

NCT06229379

2023KYPJ283

Details and patient eligibility

About

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.

Full 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.

Enrollment: 84 patients

Sex: All

Ages: 18 to 25 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• All undergraduate students from Sun Yat-sen University who participate in the ophthalmological internship.

Exclusion criteria

• Students who refuse to sign informed consent.

Trial design

Primary purpose: Other

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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84 participants in 2 patient groups

"Digital twin patient"

Experimental group

Description:

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.

Treatment:

Device: "Digital twin patient"

Real patient

Other group

Description:

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.

Treatment:

Device: "Digital twin patient"

Behavioral: Interaction with real patients