xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT06015178</url>
    </required_header>
    <id_info>
      <org_study_id>2023KYPJ222</org_study_id>
      <nct_id>NCT06015178</nct_id>
    </id_info>
    <brief_title>Enhancing Medical Researchers' Self-learning With an Intelligent Language Model</brief_title>
    <official_title>A Superiority Randomized Controlled Trial of the Effect of a Novel Intelligent Language Model on the Self-learning Ability of Medical Researchers</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Zhongshan Ophthalmic Center, Sun Yat-sen University</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Zhongshan Ophthalmic Center, Sun Yat-sen University</source>
    <oversight_info>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        Solving medical scientific problems is a crucial driving force behind the advancement of&#xD;
        medical disciplines. As the complexity of scientific questions increases, an increasing&#xD;
        number of problems require interdisciplinary collaboration to be resolved. However, most&#xD;
        medical researchers lack interdisciplinary background knowledge and require substantial time&#xD;
        to systematically learn relevant knowledge and skills. Furthermore, the continuous emergence&#xD;
        of new knowledge and skills emphasizes the importance of researchers' ability for autonomous&#xD;
        learning in the medical field. Therefore, to promote the development of medical disciplines,&#xD;
        there is an urgent need for an effective method to enhance researchers' self-directed&#xD;
        learning abilities for conducting interdisciplinary research.&#xD;
  &#xD;
        The next-generation artificial intelligence language models, exemplified by ChatGPT, hold&#xD;
        great potential in assisting researchers to access knowledge and information from various&#xD;
        domains. Whether researchers can leverage such AI tools to enhance their self-directed&#xD;
        learning abilities for conducting interdisciplinary research remains to be further explored.&#xD;
        Additionally, concerns have been raised regarding the potential degradation of cognitive&#xD;
        abilities through their use, although valid evidence is currently lacking.&#xD;
  &#xD;
        To investigate whether AI tools, represented by ChatGPT, can effectively assist medical&#xD;
        researchers in conducting interdisciplinary research and whether their usage may negatively&#xD;
        impact researchers' cognitive abilities, a randomized controlled trial is warranted. This&#xD;
        trial aims to ascertain the potential benefits and risks associated with utilizing AI tools&#xD;
        in the medical research domain.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">August 20, 2023</start_date>
    <completion_date type="Anticipated">April 30, 2024</completion_date>
    <primary_completion_date type="Anticipated">October 31, 2023</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>Randomized</allocation>
      <intervention_model>Parallel Assignment</intervention_model>
      <primary_purpose>Other</primary_purpose>
      <masking>Single (Outcomes Assessor)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>completion rate</measure>
      <time_frame>through study completion, an average of 9 months</time_frame>
      <description>The number of people who completed the task within the given time / the total number of people in the group</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Feasibility of the research program</measure>
      <time_frame>through study completion, an average of 9 months</time_frame>
      <description>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.</description>
    </secondary_outcome>
    <number_of_arms>2</number_of_arms>
    <enrollment type="Anticipated">60</enrollment>
    <condition>Medical Artificial Intelligence</condition>
    <condition>Interdisciplinary Research</condition>
    <condition>Self-Directed Learning</condition>
    <arm_group>
      <arm_group_label>Intelligent Language Model Group</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <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.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Control Group</arm_group_label>
      <arm_group_type>Placebo Comparator</arm_group_type>
      <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.</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Intelligent Language Model</intervention_name>
      <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.</description>
      <arm_group_label>Intelligent Language Model Group</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>control</intervention_name>
      <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.</description>
      <arm_group_label>Control Group</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Junior ophthalmologist with 1-3 years of clinical experience&#xD;
  &#xD;
            -  20-28 years old, regardless of gender&#xD;
  &#xD;
            -  No prior experience in interdisciplinary research involving digital medicine&#xD;
  &#xD;
            -  Self-reported a minimum of 20 hours of participation in this study during the trial&#xD;
               period&#xD;
  &#xD;
            -  Agree to participate in this study and sign informed consent&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Individuals with reading difficulties or reading disabilities&#xD;
  &#xD;
            -  Reluctance to participate in this study&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>20 Years</minimum_age>
      <maximum_age>28 Years</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>wenben chen, Doctor</last_name>
      <phone>+8618819472798</phone>
      <email>weberchan@foxmail.com</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>yuanjun shang, Doctor</last_name>
      <phone>+8613003970091</phone>
      <email>shangyj@mail2.sysu.edu.cn</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Zhongshan Ophthalmic Center, Sun Yat-sen University</name>
        <address>
          <city>Guangzhou</city>
          <state>Guangdong</state>
          <zip>510060</zip>
          <country>China</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <verification_date>July 2023</verification_date>
    <study_first_submitted>August 21, 2023</study_first_submitted>
    <study_first_submitted_qc>August 28, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 29, 2023</study_first_posted>
    <last_update_submitted>August 28, 2023</last_update_submitted>
    <last_update_submitted_qc>August 28, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 29, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>medical artificial intelligence</keyword>
    <keyword>Large Language Model</keyword>
    <keyword>superiority randomized controlled trial</keyword>
    <keyword>Self-Directed Learning</keyword>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT06015178
  org study id: 2023KYPJ222
  nct id: NCT06015178
  lead sponsor: 
  is fda regulated drug: No
  is fda regulated device: No
  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.
  allocation: Randomized
  intervention model: Parallel Assignment
  primary purpose: Other
  masking: Single (Outcomes Assessor)
  measure: completion rate
  time frame: through study completion, an average of 9 months
  description: The number of people who completed the task within the given time / the total number of people in the group
  measure: Feasibility of the research program
  time frame: through study completion, an average of 9 months
  description: 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.
  arm group label: Intelligent Language Model Group
  arm group type: Experimental
  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.
  arm group label: Control Group
  arm group type: Placebo Comparator
  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.
  intervention type: Other
  intervention name: Intelligent Language Model
  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.
  arm group label: Intelligent Language Model Group
  intervention type: Other
  intervention name: control
  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.
  arm group label: Control Group
  criteria: 
  gender: All
  minimum age: 20 Years
  maximum age: 28 Years
  healthy volunteers: Accepts Healthy Volunteers
  last name: wenben chen, Doctor
  phone: +8618819472798
  email: weberchan@foxmail.com
  last name: yuanjun shang, Doctor
  phone: +8613003970091
  email: shangyj@mail2.sysu.edu.cn
  facility: 
  country: China
  responsible party type: Sponsor
  sharing ipd: No