[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-405728-105":59,"doc-detail-405728-en":130},{"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":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","how-does-medical-artificial-intelligence-revolutionize-physician-productivity","How Does Medical Artificial Intelligence Revolutionize Physician Productivity?","","Medical artificial intelligence is assessed for its effects on physician workload and patient-care quality through a meta-analysis of empirical studies. Results show meaningful reductions in repetitive interpretation and documentation time, including about a 40% decrease with generative AI-based electronic medical record systems and 28.8% reduction with voice recognition and AI scribing. Administrative burden linked to burnout declines by over 30%, while AI interpretation shortens times across imaging and lab workflows and can support diagnostic sensitivity and specificity. The evidence informs policy toward resource efficiency and a resilient healthcare system.",{"@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/how-does-medical-artificial-intelligence-revolutionize-physician-productivity/405728/",{"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/how-does-medical-artificial-intelligence-revolutionize-physician-productivity/405728.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does medical AI reduce physician workload and diagnostic time?","Question",{"text":112,"@type":113},"AI reduces workload by automating repetitive interpretation and documentation, including generative EMR automation and voice recognition/AI scribing for charting. It also uses filtering to lessen interpretation review burden.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What reported time savings are associated with AI in electronic medical records and charting?",{"text":117,"@type":113},"Automated generative AI-based electronic medical record systems reduce documentation time by about 40%. Voice recognition and AI scribing reduce patient charting time by 28.8%.",{"name":119,"@type":110,"acceptedAnswer":120},"How does AI improve efficiency in radiology and other interpretation tasks?",{"text":121,"@type":113},"AI-based interpretation decreases interpretation time for abnormal contrast-enhanced brain CT lesions, lung lesions, and analysis time for peripheral blood smears. It also shortens manual review through automatic filtering, with reported reductions across pulmonary nodules, endometrial slides, and epilepsy electroencephalography evaluation.","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},405728,1790812585,{"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":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"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":46,"preview_page_count":4},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Review Article  \nYonsei Med J 2026 Jan;67(1):1-8 [https://doi.org/10.3349/ymj.2025.0389](https://doi.org/10.3349/ymj.2025.0389)  \npISSN: 0513-5796 · eISSN: 1976-2437  \n\n| How Does Medical Artificial Intelligence Revolutionize Physician Productivity?\u003Cbr>Ji-Yeun Lim1, Kye-hyun Kim1, and Seog-Kyun Mun1,2\u003Cbr>1Research Institute for Healthcare Policy, Korean Medical Association, Seoul;\u003Cbr>2Department of Otorhinolaryngology-Head and Neck Surgery, College of Medicine, Chung-Ang University, Seoul, Korea. |\n| --- |\n| This study examines the impact of medical artificial intelligence (AI) on physician workload and the quality of patient care. A meta-analysis of empirical studies found that AI significantly reduces physician workload and diagnostic time by automating repetitive interpretation and documentation processes, freeing clinicians to focus solely on patients. Automated generative AI-based electronic medical record systems reduce documentation time by approximately 40%, while voice recognition and AI scribing technologies reduce patient charting time by 28.8%. This reduces administrative burden, a major cause of physician burnout, by more than 30%. In radiology, AI-based interpretation reduced the interpretation time for abnormal contrast-enhanced brain CT lesions by 11.23%, the interpretation time for lung lesions by 52.82%, and the analysis time for peripheral blood smears by 61%. Importantly, these time savings occur naturally and, in some cases, improve diagnostic accuracy for major diseases (e.g., lung nodules, brain lesions, and breast cancer). Furthermore, AI minimizes the workload of interpretation through its automatic filtering function. This includes a 77.4%–86.7% reduction in review time for pulmonary nodules, a 51.3%–72.9% reduction in endometrial slide screening time, and an 86% saving in manual review time for epilepsy electroencephalography evaluation. These findings confirm that AI is establishing itself as a reliable tool that simultaneously improves physician efficiency, diagnostic efficiency, and clinical accuracy. Therefore, future healthcare policies regarding AI should not simply focus on expanding the workforce, but should adopt a strategic approach, optimizing resource efficiency and building a more resilient healthcare system.\u003Cbr>KeyWords: Artificial intelligence; physicians; workload; diagnosis, sensitivity and specificity; quality improvement;\u003Cbr>health services accessibility |\n\nINTRODUCTION  \nMedical artificial intelligence (AI) is expected to evolve from hardware to software and services,1 and its market size is projected to grow by 36.83% annually from 2025 to 2034.2 This growth trend is anticipated to continue as AI contributes to the improvement of hospital systems.3 Medical AI is widely used  \nReceived: October 20, 2025 Revised: November 18, 2025  \nAccepted: November 20, 2025 Published online: December 8, 2025  \nCorresponding author: Seog-Kyun Mun, MD, PhD, Department of Otorhinolaryngology-Head and Neck Surgery, College of Medicine, Chung-Ang University, 102 Heukseok-ro, Dongjak-gu, Seoul 06973, Korea.  \nE-mail: [entdoctor@cau.ac.kr](entdoctor@cau.ac.kr)  \n•The authors have no potential conflicts of interest to disclose.© Copyright: Yonsei University College of Medicine 2026  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ([https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)[ ](https://creativecommons.org/licenses/)[by-nc/4.0](by-nc/4.0)) [which](which) permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nin fields such as medical imaging and diagnosis, patient databased risk analysis, virtual medical assistance, inpatient treatment and hospital management, cybersecurity, drug discovery, and research.4 Particularly, the size of the computer vision market, including fundus cameras, echocardiography, and angiography devic","cbCaikiPLt3aZaqX","https://ap.wps.com/l/cbCaikiPLt3aZaqX","pdf",234859,"English","# Overview and purpose\n## Mechanisms affecting workload and care quality\n# Current status of medical AI utilization\n## Administrative applications in South Korea\n## Medical imaging and diagnostic support","[{\"question\":\"How does medical AI reduce physician workload and diagnostic time?\",\"answer\":\"AI reduces workload by automating repetitive interpretation and documentation, including generative EMR automation and voice recognition/AI scribing for charting. It also uses filtering to lessen interpretation review burden.\"},{\"question\":\"What reported time savings are associated with AI in electronic medical records and charting?\",\"answer\":\"Automated generative AI-based electronic medical record systems reduce documentation time by about 40%. Voice recognition and AI scribing reduce patient charting time by 28.8%.\"},{\"question\":\"How does AI improve efficiency in radiology and other interpretation tasks?\",\"answer\":\"AI-based interpretation decreases interpretation time for abnormal contrast-enhanced brain CT lesions, lung lesions, and analysis time for peripheral blood smears. It also shortens manual review through automatic filtering, with reported reductions across pulmonary nodules, endometrial slides, and epilepsy electroencephalography evaluation.\"}]","How Does Medical Artificial Intelligence Revolutionize Physician Productivity? | PDF",1790561621]