Enhancing Medical Researchers' Self-learning With an Intelligent Language Model
A Superiority Randomized Controlled Trial of the Effect of a Novel Intelligent Language Model on the Self-learning Ability of Medical Researchers
1 other identifier
interventional
60
1 country
1
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P25-P50 for not_applicable
Started Aug 2023
Shorter than P25 for not_applicable
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
August 21, 2023
CompletedFirst Posted
Study publicly available on registry
August 29, 2023
CompletedStudy Start
First participant enrolled
August 30, 2023
CompletedPrimary Completion
Last participant's last visit for primary outcome
October 31, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
April 30, 2024
CompletedNovember 13, 2023
July 1, 2023
2 months
August 21, 2023
November 9, 2023
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
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 Outcomes (1)
Feasibility of the research program
through study completion, an average of 9 months
Study Arms (2)
Intelligent Language Model Group
EXPERIMENTALSubjects 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
PLACEBO COMPARATORSubjects 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.
Interventions
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.
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.
Eligibility Criteria
You may qualify if:
- Junior ophthalmologist with 1-3 years of clinical experience
- 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
You may not qualify if:
- Individuals with reading difficulties or reading disabilities
- Reluctance to participate in this study
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, 510060, China
Related Publications (1)
Shang Y, Lin Y, Li R, Shang Y, Li M, Zhao L, Jin L, Xu A, Liu D, Wu Q, Luo M, Pang J, Bi S, He Y, Xu M, Chen X, Cao Z, Yu S, Zhao J, Lai Y, Chen W, Lin H. The effectiveness of large language models in medical AI research for physicians: A randomized controlled trial. Cell Rep Med. 2025 Dec 16;6(12):102469. doi: 10.1016/j.xcrm.2025.102469. Epub 2025 Nov 26.
PMID: 41308643DERIVED
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- SINGLE
- Who Masked
- OUTCOMES ASSESSOR
- Purpose
- OTHER
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
August 21, 2023
First Posted
August 29, 2023
Study Start
August 30, 2023
Primary Completion
October 31, 2023
Study Completion
April 30, 2024
Last Updated
November 13, 2023
Record last verified: 2023-07
Data Sharing
- IPD Sharing
- Will not share