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

Not Applicable

Recruiting

• Conditions: Self-Directed Learning; Interdisciplinary Research; Medical Artificial Intelligence
• Interventions: Other: Intelligent Language Model; Other: control

• Registration Number: NCT06015178
• Lead Sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Status Verified: 2023-07
• Study First Submitted: 2023-08-21
• Study First Submitted QC: 2023-08-28
• Study First Posted: 2023-08-29
• Study Start: 2023-08-30
• Primary Completion: 2023-10-31
• Last Update Submitted: 2023-11-09
• Last Update Posted: 2023-11-13
• Study Completion: 2024-04-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 60

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

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Exclusion Criteria

• Individuals with reading difficulties or reading disabilities
• Reluctance to participate in this study

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Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Intelligent Language Model Group	Intelligent Language Model	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.
Control Group	control	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.

Primary Outcome Measures

Name	Time	Method
completion rate	through study completion, an average of 9 months	The number of people who completed the task within the given time / the total number of people in the group

Secondary Outcome Measures

Name	Time	Method
Feasibility of the research program	through study completion, an average of 9 months	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.

Trial Locations

Locations (1)

Zhongshan Ophthalmic Center, Sun Yat-sen University; 🇨🇳 Guangzhou, Guangdong, China