{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T14:25:21Z","timestamp":1793715921231,"version":"4.6.1"},"reference-count":30,"publisher":"American Medical Association (AMA)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Diagnosing solid lesions in the pancreas via endoscopic ultrasonographic (EUS) images is challenging. Artificial intelligence (AI) has the potential to help with such diagnosis, but existing AI models focus solely on a single modality.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To advance the clinical diagnosis of solid lesions in the pancreas through developing a multimodal AI model integrating both clinical information and EUS images.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n In this randomized crossover trial conducted from January 1 to June 30, 2023, from 4 centers across China, 12 endoscopists of varying levels of expertise were randomly assigned to diagnose solid lesions in the pancreas with or without AI assistance. Endoscopic ultrasonographic images and clinical information of 439 patients from 1 institution who had solid lesions in the pancreas between January 1, 2014, and December 31, 2022, were collected to train and validate the joint-AI model, while 189 patients from 3 external institutions were used to evaluate the robustness and generalizability of the model.<\/jats:p>\n <\/jats:sec>\n \n Intervention<\/jats:title>\n Conventional or AI-assisted diagnosis of solid lesions in the pancreas.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n In the retrospective dataset, the performance of the joint-AI model was evaluated internally and externally. In the prospective dataset, diagnostic performance of the endoscopists with or without the AI assistance was compared.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The retrospective dataset included 628 patients (400 men [63.7%]; mean [SD] age, 57.7 [27.4] years) who underwent EUS procedures. A total of 130 patients (81 men [62.3%]; mean [SD] age, 58.4 [11.7] years) were prospectively recruited for the crossover trial. The area under the curve of the joint-AI model ranged from 0.996 (95% CI, 0.993-0.998) in the internal test dataset to 0.955 (95% CI, 0.940-0.968), 0.924 (95% CI, 0.888-0.955), and 0.976 (95% CI, 0.942-0.995) in the 3 external test datasets, respectively. The diagnostic accuracy of novice endoscopists was significantly enhanced with AI assistance (0.69 [95% CI, 0.61-0.76] vs 0.90 [95% CI, 0.83-0.94];\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001), and the supplementary interpretability information alleviated the skepticism of the experienced endoscopists.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n In this randomized crossover trial of diagnosing solid lesions in the pancreas with or without AI assistance, the joint-AI model demonstrated positive human-AI interaction, which suggested its potential to facilitate a clinical diagnosis. Nevertheless, future randomized clinical trials are warranted.<\/jats:p>\n <\/jats:sec>\n \n Trial Registration<\/jats:title>\n \n ClinicalTrials.gov Identifier:\n NCT05476978<\/jats:ext-link>\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2024.22454","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T13:01:27Z","timestamp":1721394087000},"page":"e2422454","source":"Crossref","is-referenced-by-count":62,"title":["Diagnosing Solid Lesions in the Pancreas With Multimodal Artificial Intelligence"],"prefix":"10.1001","volume":"7","author":[{"given":"Haochen","family":"Cui","sequence":"first","affiliation":[{"name":"Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Si","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunlu","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronghua","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengtao","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of California San Diego, La Jolla"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenlong","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sideng","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Algoma University, Sault Ste. 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