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

Sun Yat-sen University

Status: Enrolling

Conditions: Interdisciplinary Research; Self-Directed Learning; Medical Artificial Intelligence

Treatments: Other: control; Other: Intelligent Language Model

Study type: Interventional

Funder types: Other

Identifiers

NCT06015178

2023KYPJ222

Details and patient eligibility

About

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.

Enrollment: 60 estimated patients

Sex: All

Ages: 20 to 28 years old

Volunteers: Accepts Healthy Volunteers

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

Trial design

Primary purpose: Other

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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60 participants in 2 patient groups, including a placebo group

Intelligent Language Model Group

Experimental group

Description:

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.

Treatment:

Other: Intelligent Language Model

Control Group

Placebo Comparator group

Description:

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.

Treatment:

Other: control