Enhancing Medical Researchers' Self-learning With an Intelligent Language Model

Study Overview
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Brief Summary
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Solving medical scientific problems is a crucial driving force behind the advancement of medical disciplines. As the complexity of scientific questions increases, an increasing number of problems require interdisciplinary collaboration to be resolved. However, most medical researchers lack interdisciplinary background knowledge and require substantial time to systematically learn relevant knowledge and skills. Furthermore, the continuous emergence of new knowledge and skills emphasizes the importance of researchers' ability for autonomous learning in the medical field. Therefore, to promote the development of medical disciplines, there is an urgent need for an effective method to enhance researchers' self-directed learning abilities for conducting interdisciplinary research. The next-generation artificial intelligence language models, exemplified by ChatGPT, hold great potential in assisting researchers to access knowledge and information from various domains. Whether researchers can leverage such AI tools to enhance their self-directed learning abilities for conducting interdisciplinary research remains to be further explored. Additionally, concerns have been raised regarding the potential degradation of cognitive abilities through their use, although valid evidence is currently lacking. To investigate whether AI tools, represented by ChatGPT, can effectively assist medical researchers in conducting interdisciplinary research and whether their usage may negatively impact researchers' cognitive abilities, a randomized controlled trial is warranted. This trial aims to ascertain the potential benefits and risks associated with utilizing AI tools in the medical research domain.

Official Title
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A Superiority Randomized Controlled Trial of the Effect of a Novel Intelligent Language Model on the Self-learning Ability of Medical Researchers

Conditions
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Medical Artificial Intelligence, Interdisciplinary Research, Self-Directed Learning

Intervention / Treatment
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* Other: Intelligent Language Model
* Other: control


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Junior ophthalmologist with 1-3 years of clinical experience 20-28 years old, regardless of gender No prior experience in interdisciplinary research involving digital medicine Self-reported a minimum of 20 hours of participation in this study during the trial period Agree to participate in this study and sign informed consent Exclusion Criteria: Individuals with reading difficulties or reading disabilities Reluctance to participate in this study

Ages Eligible for Study
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Minimum Age: 20 Years
Maximum Age: 28 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Other
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Intelligent Language Model Group<br>Subjects must use the intelligent language model to complete the retrieval and protocol design execution of an interdisciplinary task, in addition to Google search, literature search and book query. | Other: Intelligent Language Model<br>* Subjects must use the intelligent language model to complete the retrieval and protocol design execution of an interdisciplinary task, in addition to Google search, literature search and book query.<br>|
| Placebo Comparator: Control Group<br>Subjects can only use Google search, literature retrieval and book query, and cannot use any AI-driven conversational natural language processing tools to complete the retrieval and protocol design execution of an interdisciplinary task. | Other: control<br>* Subjects can only use Google search, literature retrieval and book query, and cannot use any AI-driven conversational natural language processing tools to complete the retrieval and protocol design execution of an interdisciplinary task.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| completion rate | The number of people who completed the task within the given time / the total number of people in the group | through study completion, an average of 9 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Feasibility of the research program | The feasibility of the scheme is scored by a scoring group composed of experts. The feasibility is divided into 1-5 points according to the correctness and integrity of the key steps and details of the test. The higher the score, the higher the feasibility. The 1 point represents more than half of the key steps are missing or wrong, and the 5 point represents all the key steps and the details are appropriate. | through study completion, an average of 9 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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medical artificial intelligence, Large Language Model, superiority randomized controlled trial, Self-Directed Learning