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In resource-constrained settings, AI supports large orthopedic databases and outcome registries to enable data-driven research and machine-learning models. AI-driven adaptive learning personalizes training pathways, while AI-enhanced virtual reality simulations and machine-learning skill evaluation provide safe practice and quantitative feedback. Large language models offer decision support for diagnosis, simulations, and postoperative risk assessment, though adoption is limited by cost, data and computing demands, regulatory uncertainty, privacy and cyber risk, and “black-box” interpretability.",{"@graph":69,"@context":122},[70,84,105],{"@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-ai-as-a-catalyst-for-orthopedic-residency-training/449307/",{"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-ai-as-a-catalyst-for-orthopedic-residency-training/449307.png","ImageObject",300,407,{"name":92,"@type":93},"Ben ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How can AI improve orthopedic residency training in resource-constrained environments?","Question",{"text":112,"@type":113},"AI can enable large databases and registries, supporting data-driven research and locally tailored machine learning models. It also supports adaptive learning platforms and VR-based training with objective feedback.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What role do adaptive learning platforms play for orthopedic residents?",{"text":117,"@type":113},"They analyze individual resident performance to deliver personalized learning pathways. They tailor content and assessments and can align rotations with specific training needs.",{"name":119,"@type":110,"acceptedAnswer":120},"What challenges hinder adoption of AI and AI-enabled VR in surgical training?",{"text":121,"@type":113},"Barriers include high development and computing costs, the need for vast datasets, unpredictable processes requiring ongoing updates, unclear regulations, and limitations from data privacy, cyberattack risks, and insufficient faculty competency. AI model “black-box” outputs also complicate safe use.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},449307,1790819752,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":81,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":39,"preview_page_count":4},2336478951081,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","EDITORIAL  \nArtificial Intelligence (AI) as a Catalyst for Orthopedic Residency Training  \nAdel Ebrahimpour Md 1,2; Mohammadreza Dolikhani Md1,2, Ghazaleh Shakibamaram Md 1,2 1. Emergency Care Promotion Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran  \n2. Department of Orthopedic Surgery, Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.  \nMedical residents are among the most valuable  \nhuman resources in healthcare, particularly within  \nacademic medical centers. Residency training is key to becoming a competent orthopedic surgeon; however, it is both time-consuming and costly. Limited access to professional mentorship and resources hampers efforts to enhance orthopedic treatment and reduce health disparities, especially in developing countries. 1 Meanwhile, artificial intelligence (AI) technologies are rapidly revolutionizing medical education in financially secure regions; however, their implementation in developing nations is constrained by financial, infrastructural, and technological obstacles. 2  \nDespite these constraints, practical AI technologies can significantly enhance orthopedic residency training in resource-constrained environments. AI can facilitate the creation of large databases and registries in orthopedic institutions, promoting data-driven research and quality improvement initiatives. These registries facilitate tracking outcomes across broad patient populations and provide comprehensive datasets for developing machine learning models tailored to local patient demographics. 3,4  \nAdaptive Learning  \nLeveraging these digital foundations, AI-driven adaptive learning platforms, integral to an innovative learning environment, deliver personalized educational pathways by analyzing individual resident performance. 5 This approach enhances skill acquisition, even in resourceconstrained settings. These platforms can tailor content and assessments to the learner's needs, enhance the efficiency  \nand effectiveness of orthopedic training programs, and assign residents to rotations that align most closely with their training needs.  \nVirtual Reality  \nVirtual reality (VR) simulations are validated for their efficacy in surgical training. 6 At the same time, they give residents a unique opportunity to work on rare or complex cases they might not encounter elsewhere. This will help them gain hands-on experience in a safe, replicable environment without expensive equipment.  \nMoreover, advanced surgical skill evaluation systems that use machine learning algorithms on recorded surgical procedures can provide residents with accurate, timely feedback. This reduces reliance on ongoing in-person supervision and enhances training quality, particularly in institutions with limited access to qualified instructors. 7  \nThe integration of AI into VR is changing surgical education by providing individualized, dynamic instruction. AI-driven VR systems assess learner movements in realtime using machine learning algorithms to provide actionable feedback. These tools evaluate surgical precision, speed, and adaptability to issues such as hemorrhage and anatomical changes, making training more realistic and successful. 8 Research indicates that VR simulations with AI-based feedback improve surgical outcomes. A study found that AI feedback improves student performance more effectively than instructor-led education. 9 Unlike traditional in-person supervision, AIbased feedback provides regular, quantitative performance metrics for more objective judgments. 9  \nHowever, AI and VR adoption in surgical training faces obstacles. High development costs, vast datasets for AI training, and unpredictable processes may require ongoing system changes. AI engineers and surgical professionals must work together to improve these systems.  \nIntegration of AI and Practice  \nLarge language models (LLMs), such as GPT-5 and Gemini 2.5, can address training issues for orthopedic residents ","cbCainH8mZj2R2vJ","https://ap.wps.com/l/cbCainH8mZj2R2vJ","pdf",564868,"English","# Introduction\n# Adaptive Learning\n# Virtual Reality\n# Integration of AI and Practice\n# Conclusion","[{\"question\":\"How can AI improve orthopedic residency training in resource-constrained environments?\",\"answer\":\"AI can enable large databases and registries, supporting data-driven research and locally tailored machine learning models. It also supports adaptive learning platforms and VR-based training with objective feedback.\"},{\"question\":\"What role do adaptive learning platforms play for orthopedic residents?\",\"answer\":\"They analyze individual resident performance to deliver personalized learning pathways. They tailor content and assessments and can align rotations with specific training needs.\"},{\"question\":\"What challenges hinder adoption of AI and AI-enabled VR in surgical training?\",\"answer\":\"Barriers include high development and computing costs, the need for vast datasets, unpredictable processes requiring ongoing updates, unclear regulations, and limitations from data privacy, cyberattack risks, and insufficient faculty competency. AI model “black-box” outputs also complicate safe use.\"}]","Artificial Intelligence (AI) as a Catalyst for Orthopedic Residency Training | PDF",1790729813]