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The perspective emphasizes clinician advocacy for high-quality data, clear automation boundaries, and human oversight, while calling for AI governance policies and interdisciplinary collaboration. AI literacy is framed as a no-regret investment that improves care while reclaiming clinician time for the human dimensions of medicine.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/the-infectious-diseases-orchestrator-embracing-ai-literacy-in-the-agentic-era/444504/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/the-infectious-diseases-orchestrator-embracing-ai-literacy-in-the-agentic-era/444504.png","ImageObject",300,407,{"name":42,"@type":43},"Marglet","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-31","2026-09-29",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What does “agentic AI” mean for infectious diseases practice?","Question",{"text":63,"@type":64},"In the document, agentic AI is described as systems that can perceive, reason, and act within clinical environments, moving beyond predictive tools.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Why should infectious diseases clinicians develop AI literacy?",{"text":68,"@type":64},"The perspective argues clinicians need AI literacy to guide AI agents in patient care, ensuring data quality, appropriate automation boundaries, and human oversight in critical decisions.",{"name":70,"@type":61,"acceptedAnswer":71},"What problems can arise when AI is adopted passively in healthcare?",{"text":72,"@type":64},"The document highlights risks such as alert fatigue from false positives, poor external performance, documentation inaccuracies from text errors, and algorithmic bias that may worsen inequities.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},444504,1790760010,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":111,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":143,"read_time":117,"preview_page_count":4},2336477982702,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Open Forum Infectious Diseases  \nPERSPECTIVES  \nThe Infectious Diseases Orchestrator: Embracing AI Literacy in the Agentic Era  \nJohn J. Hanna1,2,3,4, and Richard J. Medford1,2,3,4,  \n1Information Services, ECU Health, Greenville, North Carolina, USA, 2Department of Internal Medicine, ECU Brody School of Medicine, Greenville, North Carolina, USA, 3Clinical Informatics Center, University of Texas Southwestern, Dallas, Texas, USA, and 4Department of Internal Medicine, University of Texas Southwestern, Dallas, Texas, USA  \n\n| Artificial intelligence (AI) is rapidly transforming healthcare, with agentic AI systems positioned to perceive, reason, and act within clinical environments. For infectious diseases (ID) clinicians, agentic AI presents both opportunity and imperative; to embrace AI literacy and remain actively engaged in shaping their design rather than becoming passive adopters in clinical care, antimicrobial stewardship, and infection control. Historical examples show that professions failing to adapt to automation faced challenges, highlighting the urgency for ID specialists to understand AI’s evolving role. While AI can streamline documentation, surveillance, and decision support, clinicians must advocate for high-quality data, define appropriate automation boundaries, and ensure human oversight in critical decisions. ID communities should lead efforts to educate clinicians, establish AI governance policies in ID operational practices, and foster interdisciplinary collaboration to guide responsible AI integration. AI literacy is the “no-regret” investment that will enable clinicians to lead this transformation—ensuring that AI supports, augments, and, when appropriate, automates the repetitive, searchable, and time-consuming tasks. The future of ID practice will be defined by how effectively clinicians leverage AI to enhance care, promote equitable access, and reclaim time for the human dimensions of medicine.\u003Cbr>Keywords. AI agents; artificial intelligence; infectious diseases practice; large language models; natural language processing. |  |  |\n| --- | --- | --- |\n| Infectious diseases (ID) practice is entering an inflection point as artificial intelligence (AI) advances from predictive tools to agentic systems that can perceive, reason, and act within clinical environments [ 1–4]. These capabilities are increasingly incorporated within electronic health record (EHR) ecosystems [5] to streamline decision-making processes, enhance documentation efficiency, and provide realtime clinical insights.\u003Cbr>These rapidly evolving AI technologies signal a fundamental shift in how medicine—and particularly ID—will be practiced. Unlike many other specialties that\u003Cbr>Received 27 August 2025; accepted 08 December 2025; published online 26 December 2025\u003Cbr>Correspondence: John J. Hanna, MD, Information Services, ECU Health, 2190 Beasley Dr, Greenville, NC 27834 (john. [hanna@ecuhealth.org](hanna@ecuhealth.org)) .\u003Cbr>Open Forum Infectious Diseases®\u003Cbr>© The Author(s) 2025. Published by Oxford University Press on behalf of Infectious Diseases Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/4.0/](creativecommons.org/licenses/by/4.0/)), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. [https://doi.org/10.1093/ofid/ofaf794](https://doi.org/10.1093/ofid/ofaf794) | rely on well-defined clinical pathways, ID practice is inherently data-intensive, decision-heavy, and context-dependent, often requiring real-time synthesis of epidemiology, microbiology, antimicrobial resistance patterns, and detailed clinical risk factors to make optimal patient care decisions. The very nature of ID work—navigating diagnostic uncertainty, responding to outbreaks, managing antimicrobial stewardship programs, and adapting to emerging pathogens—alig","cbCaikh4RwqEiR9H","https://ap.wps.com/l/cbCaikh4RwqEiR9H","pdf",734887,"English","# Perspectives\n## The shift to agentic AI in infectious diseases\n## Why AI literacy is an essential core competency\n## Risks of passive adoption and the need for governance\n## Clinician-led orchestration and future implications","[{\"question\":\"What does “agentic AI” mean for infectious diseases practice?\",\"answer\":\"In the document, agentic AI is described as systems that can perceive, reason, and act within clinical environments, moving beyond predictive tools.\"},{\"question\":\"Why should infectious diseases clinicians develop AI literacy?\",\"answer\":\"The perspective argues clinicians need AI literacy to guide AI agents in patient care, ensuring data quality, appropriate automation boundaries, and human oversight in critical decisions.\"},{\"question\":\"What problems can arise when AI is adopted passively in healthcare?\",\"answer\":\"The document highlights risks such as alert fatigue from false positives, poor external performance, documentation inaccuracies from text errors, and algorithmic bias that may worsen inequities.\"}]","The Infectious Diseases Orchestrator - Embracing AI Literacy in the Agentic Era | PDF",1790708269]