Artificial Intelligence–Powered Training Database for Clinical Thinking: App Development Study
Abstract BackgroundWith the development of artificial intelligence (AI),ntered the era of intelligent medicine ObjectiveThis study aimed to introduce an app named “XueYiKu,” which includes consultations, physical examinations, auxiliary examinations, and diagnosis,...
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JMIR Publications
2025-01-01
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| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2025/1/e58426 |
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| author | Heng Wang Danni Zheng Mengying Wang Hong Ji Jiangli Han Yan Wang Ning Shen Jie Qiao |
| author_facet | Heng Wang Danni Zheng Mengying Wang Hong Ji Jiangli Han Yan Wang Ning Shen Jie Qiao |
| author_sort | Heng Wang |
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Abstract
BackgroundWith the development of artificial intelligence (AI),ntered the era of intelligent medicine
ObjectiveThis study aimed to introduce an app named “XueYiKu,” which includes consultations, physical examinations, auxiliary examinations, and diagnosis, incorporating AI and actual complete hospital medical records to build an online-learning platform using human-computer interaction.
MethodsThe “XueYiKu” app was designed as a contactless, self-service, trial-and-error system application based on actual complete hospital medical records and natural language processing technology to comprehensively assess the “clinical competence” of residents at different stages. Case extraction was performed at a hospital’s case data center, and the best-matching cases were differentiated through natural language processing, word segmentation, synonym conversion, and sorting. More than 400 teaching cases covering 65 kinds of diseases were released for students to learn, and the subjects covered internal medicine, surgery, gynecology and obstetrics, and pediatrics. The difficulty of learning cases was divided into four levels in ascending order. Moreover, the learning and teaching effects were evaluated using 6 dimensions covering systematicness, agility, logic, knowledge expansion, multidimensional evaluation indicators, and preciseness.
ResultsFrom the app’s first launch on the Android platform in May 2019 to the last version updated in May 2023, the total number of teacher and student users was 6209 and 1180, respectively. The top 3 subjects most frequently learned were respirology (n=606, 24.1%), general surgery (n=506, 20.1%), and urinary surgery (n=390, 15.5%). For diseases, pneumonia was the most frequently learned, followed by cholecystolithiasis (n=216, 14.1%), benign prostate hyperplasia (n=196, 12.8%), and bladder tumor (n=193, 12.6%). Among 479 students, roughly a third (n=168, 35.1%) scored in the 60 to 80 range, and half of them scored over 80 points (n=238, 49.7%). The app enabled medical students’ learning to become more active and self-motivated, with a variety of formats, and provided real-time feedback through assessments on the platform. The learning effect was satisfactory overall and provided important precedence for establishing scientific models and methods for assessing clinical thinking skills in the future.
ConclusionsThe integration of AI and medical education will undoubtedly assist in the restructuring of education processes; promote the evolution of the education ecosystem; and provide new convenient ways for independent learning, interactive communication, and educational resource sharing. |
| format | Article |
| id | doaj-art-48628148c85a42558a5f605d51822168 |
| institution | Kabale University |
| issn | 2561-326X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj-art-48628148c85a42558a5f605d518221682025-02-07T19:16:08ZengJMIR PublicationsJMIR Formative Research2561-326X2025-01-019e58426e5842610.2196/58426Artificial Intelligence–Powered Training Database for Clinical Thinking: App Development StudyHeng Wanghttp://orcid.org/0009-0001-6828-9520Danni Zhenghttp://orcid.org/0000-0003-3387-2705Mengying Wanghttp://orcid.org/0000-0003-0162-5355Hong Jihttp://orcid.org/0000-0003-3417-3615Jiangli Hanhttp://orcid.org/0000-0002-6912-4533Yan Wanghttp://orcid.org/0009-0000-2606-1125Ning Shenhttp://orcid.org/0009-0007-8409-9835Jie Qiaohttp://orcid.org/0000-0003-2126-1376 Abstract BackgroundWith the development of artificial intelligence (AI),ntered the era of intelligent medicine ObjectiveThis study aimed to introduce an app named “XueYiKu,” which includes consultations, physical examinations, auxiliary examinations, and diagnosis, incorporating AI and actual complete hospital medical records to build an online-learning platform using human-computer interaction. MethodsThe “XueYiKu” app was designed as a contactless, self-service, trial-and-error system application based on actual complete hospital medical records and natural language processing technology to comprehensively assess the “clinical competence” of residents at different stages. Case extraction was performed at a hospital’s case data center, and the best-matching cases were differentiated through natural language processing, word segmentation, synonym conversion, and sorting. More than 400 teaching cases covering 65 kinds of diseases were released for students to learn, and the subjects covered internal medicine, surgery, gynecology and obstetrics, and pediatrics. The difficulty of learning cases was divided into four levels in ascending order. Moreover, the learning and teaching effects were evaluated using 6 dimensions covering systematicness, agility, logic, knowledge expansion, multidimensional evaluation indicators, and preciseness. ResultsFrom the app’s first launch on the Android platform in May 2019 to the last version updated in May 2023, the total number of teacher and student users was 6209 and 1180, respectively. The top 3 subjects most frequently learned were respirology (n=606, 24.1%), general surgery (n=506, 20.1%), and urinary surgery (n=390, 15.5%). For diseases, pneumonia was the most frequently learned, followed by cholecystolithiasis (n=216, 14.1%), benign prostate hyperplasia (n=196, 12.8%), and bladder tumor (n=193, 12.6%). Among 479 students, roughly a third (n=168, 35.1%) scored in the 60 to 80 range, and half of them scored over 80 points (n=238, 49.7%). The app enabled medical students’ learning to become more active and self-motivated, with a variety of formats, and provided real-time feedback through assessments on the platform. The learning effect was satisfactory overall and provided important precedence for establishing scientific models and methods for assessing clinical thinking skills in the future. ConclusionsThe integration of AI and medical education will undoubtedly assist in the restructuring of education processes; promote the evolution of the education ecosystem; and provide new convenient ways for independent learning, interactive communication, and educational resource sharing.https://formative.jmir.org/2025/1/e58426 |
| spellingShingle | Heng Wang Danni Zheng Mengying Wang Hong Ji Jiangli Han Yan Wang Ning Shen Jie Qiao Artificial Intelligence–Powered Training Database for Clinical Thinking: App Development Study JMIR Formative Research |
| title | Artificial Intelligence–Powered Training Database for Clinical Thinking: App Development Study |
| title_full | Artificial Intelligence–Powered Training Database for Clinical Thinking: App Development Study |
| title_fullStr | Artificial Intelligence–Powered Training Database for Clinical Thinking: App Development Study |
| title_full_unstemmed | Artificial Intelligence–Powered Training Database for Clinical Thinking: App Development Study |
| title_short | Artificial Intelligence–Powered Training Database for Clinical Thinking: App Development Study |
| title_sort | artificial intelligence powered training database for clinical thinking app development study |
| url | https://formative.jmir.org/2025/1/e58426 |
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