{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T03:45:27Z","timestamp":1787024727212,"version":"build-2736575974"},"reference-count":14,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n \n With the development of artificial intelligence (AI),ntered the era of\n intelligent medicine<\/jats:italic>\n , and various aspects, such as medical education and talent cultivation, are also being redefined. The cultivation of clinical thinking abilities poses a formidable challenge even for seasoned clinical educators, as offline training modalities often fall short in bridging the divide between current practice and the desired ideal. Consequently, there arises an imperative need for the expeditious development of a web-based database, tailored to empower physicians in their quest to learn and hone their clinical reasoning skills.\n <\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to introduce an app named \u201cXueYiKu,\u201d 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n The \u201cXueYiKu\u201d 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 \u201cclinical competence\u201d of residents at different stages. Case extraction was performed at a hospital\u2019s 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n From the app\u2019s 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\u2019 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.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/58426","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T00:18:13Z","timestamp":1727137093000},"page":"e58426-e58426","source":"Crossref","is-referenced-by-count":4,"title":["Artificial Intelligence\u2013Powered Training Database for Clinical Thinking: App Development Study"],"prefix":"10.2196","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6828-9520","authenticated-orcid":false,"given":"Heng","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3387-2705","authenticated-orcid":false,"given":"Danni","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0162-5355","authenticated-orcid":false,"given":"Mengying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3417-3615","authenticated-orcid":false,"given":"Hong","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6912-4533","authenticated-orcid":false,"given":"Jiangli","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2606-1125","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8409-9835","authenticated-orcid":false,"given":"Ning","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2126-1376","authenticated-orcid":false,"given":"Jie","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"issue":"4","key":"R1","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1016\/j.chest.2020.05.525","article-title":"Teaching clinical reasoning and critical thinking: from cognitive theory to practical application","volume":"158","author":"Richards","journal-title":"Chest"},{"issue":"12","key":"R2","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1016\/j.amjmed.2022.08.022","article-title":"Patient-data management and clinical reasoning: neglected essential skills","volume":"135","author":"Reinus","journal-title":"Am J Med"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1111\/medu.15479","article-title":"Understanding cultural dynamics shaping clinical reasoning skills: A dialogical exploration","author":"Karunaratne","journal-title":"Med Educ"},{"issue":"1","key":"R4","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1186\/s12909-020-02138-w","article-title":"The development of clinical thinking in trainee physicians: the educator perspective","volume":"20","author":"Locke","journal-title":"BMC Med Educ"},{"issue":"23-24","key":"R5","doi-asserted-by":"publisher","first-page":"4065","DOI":"10.1111\/jocn.13901","article-title":"An integrated review of the correlation between critical thinking ability and clinical decision-making in nursing","volume":"26","author":"Lee","journal-title":"J Clin Nurs"},{"issue":"11S","key":"R6","doi-asserted-by":"publisher","first-page":"S10","DOI":"10.1097\/ACM.0000000000003630","article-title":"On artificial intelligence and deep learning within medical education","volume":"95","author":"Carin","journal-title":"Acad Med"},{"issue":"5","key":"R7","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1097\/00001888-199405000-00052","article-title":"Using diagnostic reasoning (DxR) to teach and evaluate clinical reasoning skills","volume":"69","author":"Myers","journal-title":"Acad Med"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8785127","article-title":"Optimization of online education and teaching evaluation system based on GA-BP neural network","volume":"2021","author":"Xu","journal-title":"Comput Intell Neurosci"},{"key":"R9","doi-asserted-by":"publisher","DOI":"10.3233\/SHTI230581","article-title":"Applications of artificial intelligence (AI) in medical education: a scoping review","volume":"305","author":"Nagi","journal-title":"Stud Health Technol Inform"},{"issue":"4","key":"R10","doi-asserted-by":"publisher","DOI":"10.2196\/15655","article-title":"Developing a competency-based learning and assessment system for residency training: analysis study of user requirements and acceptance","volume":"22","author":"Hsiao","journal-title":"J Med Internet Res"},{"issue":"1","key":"R11","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1111\/medu.14409","article-title":"Artificial intelligence and the adoption of new technology in medical education","volume":"55","author":"Webster","journal-title":"Med Educ"},{"issue":"1","key":"R12","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s12911-022-01797-7","article-title":"Intelligent virtual case learning system based on real medical records and natural language processing","volume":"22","author":"Wang","journal-title":"BMC Med Inform Decis Mak"},{"issue":"8","key":"R13","doi-asserted-by":"publisher","first-page":"89","DOI":"10.3969\/j.issn.1673-6036.2012.08.024","article-title":"Promoting effect of information products on case-based clinical teaching model","author":"Meng","journal-title":"J Med Inform"},{"issue":"1","key":"R14","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1186\/s12909-019-1891-5","article-title":"Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review","volume":"19","author":"Han","journal-title":"BMC Med Educ"}],"container-title":["JMIR Formative Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/formative.jmir.org\/2025\/1\/e58426","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:50:14Z","timestamp":1782841814000},"score":1,"resource":{"primary":{"URL":"https:\/\/formative.jmir.org\/2025\/1\/e58426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,3]]},"references-count":14,"URL":"https:\/\/doi.org\/10.2196\/58426","relation":{},"ISSN":["2561-326X"],"issn-type":[{"value":"2561-326X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,3]]},"article-number":"v9i4e58426"}}