[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-433599-105":59,"doc-detail-433599-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","artificial-intelligence-algorithm-improves-radiologists-bone-age-assessment-accuracy","Artificial intelligence algorithm improves radiologists’ bone age assessment accuracy","","Artificial intelligence (AI) algorithms can provide rapid and precise radiographic bone age (BA) assessment. This prospective randomized crossover study evaluated how an AI algorithm affected BA performance in radiologists with different experience levels, and examined the role of automation bias. Six radiologists assessed 200 standard BA radiographs with and without AI support, using expert mean assessments as ground truth for accuracy metrics.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/artificial-intelligence-algorithm-improves-radiologists-bone-age-assessment-accuracy/433599/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/artificial-intelligence-algorithm-improves-radiologists-bone-age-assessment-accuracy/433599.png","ImageObject",300,407,{"name":92,"@type":93},"Patrick","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How was the AI assistance condition compared with the no-AI condition?","Question",{"text":113,"@type":114},"Radiologists independently assessed the same BA test cases without AI assistance (A− B−) and with AI assistance (A+ B+), using expert-mean BA as ground truth for accuracy evaluation.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Did AI improve radiologists’ bone age accuracy across experience levels?",{"text":118,"@type":114},"Yes. Overall accuracy improved significantly for senior, mid-level, and junior radiologists with AI assistance compared with without AI assistance.",{"name":120,"@type":111,"acceptedAnswer":121},"What effect did automation bias have in the study?",{"text":122,"@type":114},"Automation bias was more likely to affect less experienced radiologists, influencing how they used AI suggestions.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},433599,1790766272,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":46,"preview_page_count":4},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470","J Chin MedAssoc  \nOriginal article  \nArtificial intelligence algorithm improves radiologists’ bone age assessment accuracy  \nTien-Yu Changa, Ting Ywan Choub,c, I-An Jend, Yeong-Seng Yuhe,f,*  \naDepartment of Radiology, Cheng-Hsin General Hospital, Taipei, Taiwan, ROC; bDepartment of Radiology, Cardinal Tien General Hospital, Taipei, Taiwan, ROC; cCollege of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan, ROC; dInstitute of Public Health, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC; eDepartment of Pediatrics, Cheng-Hsin General Hospital, Taipei, Taiwan, ROC; fDepartment of Pediatrics, National Defense Medical Center, Taipei, Taiwan, ROC  \nAbstract  \nBackground: Artificial intelligence (AI) algorithms can provide rapid and precise radiographic bone age (BA) assessment. This study assessed the effects of an AI algorithm on the BA assessment performance of radiologists, and evaluated how automation bias could affect radiologists.  \nMethods: In this prospective randomized crossover study, six radiologists with varying levels of experience (senior, mid-level, and junior) assessed cases from a test set of 200 standard BA radiographs. The test set was equally divided into two subsets: datasets A and B. Each radiologist assessed BA independently without AI assistance (A− B−) and with AI assistance (A+ B+) . We used the mean of assessments made by two experts as the ground truth for accuracy assessment; subsequently, we calculated the mean absolute difference (MAD) between the radiologists’ BA predictions and ground-truth BA and evaluated the proportion of estimates for which the MAD exceeded one year. Additionally, we compared the radiologists’ performance under conditions of early AI assistance with their performance under conditions of delayed AI assistance; the radiologists were allowed to reject AI interpretations. Results: The overall accuracy of senior, mid-level, and junior radiologists improved significantly with AI assistance than without AI assistance (MAD: 0.74 vs 0.46 years, p \u003C 0.001; proportion of assessments for which MAD exceeded 1 year: 24.0% vs 8.4%, p \u003C 0.001) . The proportion of improved BA predictions with AI assistance (16 .8%) was significantly higher than that of less accurate predictions with AI assistance (2 .3%; p \u003C 0.001) . No consistent timing effect was observed between conditions of early and delayed AI assistance. Most disagreements between radiologists and AI occurred over images for patients aged ≤8 years. Senior radiologists had more disagreements than other radiologists.  \nConclusion: The AI algorithm improved the BA assessment accuracy of radiologists with varying experience levels. Automation bias was prone to affect less experienced radiologists.  \nKeywords: Age determined by skeleton; Artificial intelligence; Radiologists  \n530 [www.ejcma.org](www.ejcma.org)[ ](www.ejcma.org)\u003Cz10.1097/JCMA.0000000000001248>  \nOriginal Article. (2025) 88:7 J Chin MedAssoc  \nLay Summary: Bone age (BA) assessment is a measure of skeletal maturity and is used to predict a child's final adult height. The Greulich and Pyle plot method is a commonly used approach but is considered tedious, time-consuming, and requires experience. Four years ago, we developed an artificial intelligence (AI) model for BA assessment. In this study, we demonstrated that the accuracy of radiologists’ BA assessments can be improved by providing BA information assigned by this AI model.  \n1. INTRODUCTION  \nRadiographic bone age (BA) assessment is central to the clinical evaluation of patients with pediatric endocrine and metabolic disorders; this assessment entails comparing a patient’s chronological age with their level of skeletal maturity on the basis of a standardized reference.1 In clinical practice, BA assessment is typically performed using either the Greulich and Pyle2 method orthe Tanner-Whitehouse 3 method3; the Greulich and Pyle method entails comparing radiographs of the left hand and wrist with an","cbCaiqfBzr7rwJC2","https://ap.wps.com/l/cbCaiqfBzr7rwJC2","pdf",899655,"English","# Abstract\n# Background\n# Methods\n## Study design\n## Accuracy evaluation\n## Automation bias assessment\n# Results\n# Conclusion\n# Lay Summary\n# Introduction","[{\"question\":\"How was the AI assistance condition compared with the no-AI condition?\",\"answer\":\"Radiologists independently assessed the same BA test cases without AI assistance (A− B−) and with AI assistance (A+ B+), using expert-mean BA as ground truth for accuracy evaluation.\"},{\"question\":\"Did AI improve radiologists’ bone age accuracy across experience levels?\",\"answer\":\"Yes. Overall accuracy improved significantly for senior, mid-level, and junior radiologists with AI assistance compared with without AI assistance.\"},{\"question\":\"What effect did automation bias have in the study?\",\"answer\":\"Automation bias was more likely to affect less experienced radiologists, influencing how they used AI suggestions.\"}]","Artificial intelligence algorithm improves radiologists’ bone age assessment accuracy | PDF",1790664751]