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Using large language model to aid in teaching medical imaging report writing.

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Title: Using large language model to aid in teaching medical imaging report writing.
Authors: Chen, Yingqian1 (AUTHOR), Xiang, Pei1 (AUTHOR), Zhou, Qin1 (AUTHOR), Li, Chang1 (AUTHOR), Zhang, Xiaoling1 (AUTHOR), Wang, Jifei1 (AUTHOR), Wang, Huanjun1 (AUTHOR), Gao, Zhenhua1 (AUTHOR), Yang, Zhiyun1 (AUTHOR), Ye, Shanshan1 (AUTHOR), Taylor, David2 (AUTHOR) prof.davidtaylor@gmu.ac.ae, Feng, Shi-Ting1 (AUTHOR) fengsht@mail.sysu.edu.cn
Source: Medical Teacher. Jun2026, Vol. 48 Issue 6, p957-966. 10p.
Subject Terms: *Academic medical centers, *Internship programs, *Teaching methods, *Medical students, *Students, *Clinical competence, *Ability, *Comparative studies, *Student attitudes, *Training, *Evaluation, Supervision of employees, Diagnostic imaging, Clinical supervision, Research funding, T-test (Statistics), Statistical sampling, Questionnaires, Fisher exact test, Natural language processing, Mann Whitney U Test, Hospitals, Statistics, Report writing, Data analysis software, Chatbots
Geographic Terms: China
Abstract: Purpose: This study aims to compare several free large language models (LLMs), identify which provides the most effective feedback, and investigate whether LLM-generated feedback can improve the accuracy and standardization of imaging reports produced by students. Methods: A randomly selected class (test group, N= 30) was asked to write an imaging report based on each typical teaching case before and after receiving feedback generated by LLM. Another randomly selected class (control group, N= 30) was asked to write an imaging report of the same case without receiving the LLM-generated feedback. The quality of the feedback generated by the 4 main free LLMs was evaluated. The residency training examination marking scale was used to evaluate the quality of the reports. A questionnaire was used to investigate whether the students were satisfied with the feedback given by LLM. Results: The feedback generated by ChatGPT 3.5, ERNIE Bot v3.5, and Tongyi v2.5 all demonstrated better structure and logic than that of Claude 3 OPUS (Mann-Whitney U Test, p < 0.05), but all exhibited some degree of hallucination. The scores of the reports in the test group were increased after receiving the feedback, and were higher than the control group (t-test, p < 0.05). Conclusion: The feedback given by LLMs can help the students critically evaluate their reports and improve their reporting skills, but should be supervised by teachers. [ABSTRACT FROM AUTHOR]
Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Using large language model to aid in teaching medical imaging report writing.
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  Data: Purpose: This study aims to compare several free large language models (LLMs), identify which provides the most effective feedback, and investigate whether LLM-generated feedback can improve the accuracy and standardization of imaging reports produced by students. Methods: A randomly selected class (test group, N= 30) was asked to write an imaging report based on each typical teaching case before and after receiving feedback generated by LLM. Another randomly selected class (control group, N= 30) was asked to write an imaging report of the same case without receiving the LLM-generated feedback. The quality of the feedback generated by the 4 main free LLMs was evaluated. The residency training examination marking scale was used to evaluate the quality of the reports. A questionnaire was used to investigate whether the students were satisfied with the feedback given by LLM. Results: The feedback generated by ChatGPT 3.5, ERNIE Bot v3.5, and Tongyi v2.5 all demonstrated better structure and logic than that of Claude 3 OPUS (Mann-Whitney U Test, p &lt; 0.05), but all exhibited some degree of hallucination. The scores of the reports in the test group were increased after receiving the feedback, and were higher than the control group (t-test, p &lt; 0.05). Conclusion: The feedback given by LLMs can help the students critically evaluate their reports and improve their reporting skills, but should be supervised by teachers. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Medical Teacher is the property of Taylor &amp; Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Value: 10.1080/0142159X.2025.2603353
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        Text: English
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    Subjects:
      – SubjectFull: Academic medical centers
        Type: general
      – SubjectFull: Internship programs
        Type: general
      – SubjectFull: Teaching methods
        Type: general
      – SubjectFull: Medical students
        Type: general
      – SubjectFull: Students
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      – SubjectFull: Clinical competence
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      – SubjectFull: Ability
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      – SubjectFull: Comparative studies
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      – SubjectFull: Student attitudes
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      – SubjectFull: Training
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      – SubjectFull: Evaluation
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      – SubjectFull: Supervision of employees
        Type: general
      – SubjectFull: Diagnostic imaging
        Type: general
      – SubjectFull: Clinical supervision
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      – SubjectFull: Research funding
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      – SubjectFull: T-test (Statistics)
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      – SubjectFull: Statistical sampling
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      – SubjectFull: Fisher exact test
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      – SubjectFull: Natural language processing
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      – SubjectFull: Mann Whitney U Test
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      – SubjectFull: Hospitals
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      – SubjectFull: Statistics
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      – SubjectFull: Report writing
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      – SubjectFull: Data analysis software
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      – SubjectFull: Chatbots
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      – SubjectFull: China
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      – TitleFull: Using large language model to aid in teaching medical imaging report writing.
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              Text: Jun2026
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