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

Sponsor
Zhongshan Ophthalmic Center, Sun Yat-sen University (Other)
Overall Status
Not yet recruiting
CT.gov ID
NCT06015178
Collaborator
(none)
60
1
2
8.3
7.2

Study Details

Study Description

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.

Condition or Disease Intervention/Treatment Phase
  • Other: Intelligent Language Model
  • Other: control
N/A

Study Design

Study Type:
Interventional
Anticipated Enrollment :
60 participants
Allocation:
Randomized
Intervention Model:
Parallel Assignment
Masking:
Single (Outcomes Assessor)
Primary Purpose:
Other
Official Title:
A Superiority Randomized Controlled Trial of the Effect of a Novel Intelligent Language Model on the Self-learning Ability of Medical Researchers
Anticipated Study Start Date :
Aug 20, 2023
Anticipated Primary Completion Date :
Oct 31, 2023
Anticipated Study Completion Date :
Apr 30, 2024

Arms and Interventions

Arm Intervention/Treatment
Experimental: Intelligent Language Model Group

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
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.

Placebo Comparator: Control Group

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
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.

Outcome Measures

Primary Outcome Measures

  1. 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

  1. 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.

Eligibility Criteria

Criteria

Ages Eligible for Study:
20 Years to 28 Years
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
Yes
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

Contacts and Locations

Locations

Site City State Country Postal Code
1 Zhongshan Ophthalmic Center, Sun Yat-sen University Guangzhou Guangdong China 510060

Sponsors and Collaborators

  • Zhongshan Ophthalmic Center, Sun Yat-sen University

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.
Responsible Party:
Zhongshan Ophthalmic Center, Sun Yat-sen University
ClinicalTrials.gov Identifier:
NCT06015178
Other Study ID Numbers:
  • 2023KYPJ222
First Posted:
Aug 29, 2023
Last Update Posted:
Aug 29, 2023
Last Verified:
Jul 1, 2023
Individual Participant Data (IPD) Sharing Statement:
No
Plan to Share IPD:
No
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Keywords provided by Zhongshan Ophthalmic Center, Sun Yat-sen University

Study Results

No Results Posted as of Aug 29, 2023