Medical teacher · 2025
Using large language model to aid in teaching medical imaging report writing.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Chen Yingqian, Xiang Pei, Zhou Qin, Li Chang, Zhang Xiaoling, Wang Jifei, Wang Huanjun, Gao Zhenhua, Yang Zhiyun, Ye Shanshan, Taylor David, Feng Shi-Ting
Аннотация
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.
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.