[{"data":1,"prerenderedAt":179},["ShallowReactive",2],{"uc-org-centers-for-disease-control-and-prevention":3},{"organization":4,"includeUnpublished":14,"evidence":15},{"slug":5,"name":6,"country":7,"region":8,"industry":9,"wikidata":10,"records":11,"useCases":12,"indexable":13},"centers-for-disease-control-and-prevention","Centers for Disease Control and Prevention","US","north-america","government","Q583725",6,7,true,false,[16,45,74,103,122,150],{"title":17,"useCases":18,"organization":20,"vendors":21,"summary":24,"stage":25,"year":26,"channels":27,"languages":29,"metrics":31,"outcomeDisclosed":13,"sources":32,"verification":37,"grade":40,"id":41,"useCaseTitles":42},"CDC: AI models distribute CDC and State Department cables",[19],"correspondence-triage-and-routing",{"name":6,"anonymized":14,"country":7,"region":8,"industry":9,"wikidata":10},[22],{"name":6,"role":23},"in-house","In the Center for Global Health, AI models read incoming CDC and State Department cables and send formatted messages to the intended recipients within seconds, with no manual step. The inventory classifies the use case as agentic AI. CDC says it cut distribution from about a day to seconds.","production",2025,[28],"internal-tools",[30],"en",[],[33],{"url":34,"title":35,"publisher":36},"https:\u002F\u002Fraw.githubusercontent.com\u002Fombegov\u002F2025-Federal-Agency-AI-Use-Case-Inventory\u002Fmain\u002FData\u002F2025_individually_reported_AI_use_cases.csv","2025 Federal AI use case inventory, HHS entry: Automating the distribution of CDC-State Department cables","Office of Management and Budget (GitHub)",{"level":38,"checkedAt":39},"source-verified","2026-11-12","B","cdc-state-department-cable-distribution",[43],{"slug":19,"title":44},"AI for inbound correspondence triage and routing",{"title":46,"useCases":47,"organization":49,"vendors":50,"summary":52,"stage":53,"year":54,"channels":55,"languages":56,"metrics":57,"outcomeDisclosed":13,"sources":66,"verification":69,"grade":40,"id":70,"useCaseTitles":71},"CDC: general purpose LLM assistant for staff",[48],"civil-servant-drafting-copilot",{"name":6,"anonymized":14,"country":7,"region":8,"industry":9,"wikidata":10},[51],{"name":6,"role":23},"CDC staff use a large language model assistant to summarize and extract information from uploaded documents, create content and develop software code. The inventory entry says the tool has saved staff an estimated 40,000 hours and returned more than 500% ROI for the agency. Staff decide how to use the generated text.","scaled",2024,[28],[30],[58],{"kpi":59,"value":60,"unit":61,"qualifier":62,"period":63,"claimant":64,"quote":65,"sourceUrl":34},"hours-saved",40000,"hours","approximately","since February 2024, as stated in the 2025 inventory","organization","Staff have used this tool to save an estimated 40,000 hours across various domains including efficiency gains with content creation, software development, and other back-of-the-house tasks within CDC.",[67],{"url":34,"title":68,"publisher":36},"2025 Federal AI use case inventory, HHS entry: CDC Chatbot",{"level":38,"checkedAt":39},"cdc-chatbot-enterprise-assistant",[72],{"slug":48,"title":73},"AI drafting copilot for civil servants for correspondence, briefings and ministerial replies",{"title":75,"useCases":76,"organization":79,"vendors":80,"summary":84,"stage":25,"year":54,"channels":85,"languages":87,"metrics":88,"outcomeDisclosed":14,"sources":89,"verification":95,"grade":40,"id":97,"useCaseTitles":98},"US Centers for Disease Control and Prevention: SmartFind knowledge bot for partner mailbox replies",[77,78],"support-knowledge-article-generation","email-and-ticket-reply-drafting",{"name":6,"anonymized":14,"country":7,"region":8,"industry":9,"wikidata":10},[81],{"name":82,"role":83},"Microsoft","platform","CDC's National Center for Immunization and Respiratory Diseases runs SmartFind, an internal knowledge bot with a SharePoint component that helps program staff manage partner emails and lets mailbox managers use a shared knowledge base. The agency lists partner mailbox email management and knowledge base maintenance as the purpose. The bot matches free text questions to agency cleared answers and flags complex or unanswerable queries for manual review. Earlier public facing versions gave agency cleared answers to public and partner questions during the COVID-19 pandemic. No outcome figures are published.",[86,28],"email",[30],[],[90,93],{"url":91,"title":92,"publisher":36},"https:\u002F\u002Fgithub.com\u002Fombegov\u002F2025-Federal-Agency-AI-Use-Case-Inventory","2025 Federal Agency AI Use Case Inventory",{"url":34,"title":94,"publisher":36},"2025 individually reported AI use cases (HHS entry, NCIRD SmartFind ChatBots, Public and Internal)",{"level":38,"checkedAt":96},"2026-09-27","cdc-smartfind-knowledge-bot",[99,101],{"slug":77,"title":100},"AI for support knowledge article generation and maintenance",{"slug":78,"title":102},"AI reply drafting for customer email and support tickets",{"title":104,"useCases":105,"organization":107,"vendors":108,"summary":109,"stage":25,"year":110,"channels":111,"languages":112,"metrics":113,"outcomeDisclosed":14,"sources":114,"verification":117,"grade":40,"id":118,"useCaseTitles":119},"US Centers for Disease Control and Prevention: generative AI stance analysis of public comments on proposed rules",[106],"public-consultation-response-analysis",{"name":6,"anonymized":14,"country":7,"region":8,"industry":9,"wikidata":10},[],"CDC reports in the 2025 federal AI use case inventory that it uses generative AI to analyze public comments on its proposed rules. For each comment the system records a stance (support, oppose or neutral), topics and sentiment, which regulatory analysts use when they review and summarize the feedback for the rulemaking record. The inventory lists the use case as deployed since July 2023. No outcome figures are published.",2023,[28],[30],[],[115],{"url":34,"title":116,"publisher":36},"2025 federal agency AI use case inventory, individually reported use cases (raw data)",{"level":38,"checkedAt":96},"cdc-public-comment-stance-analysis",[120],{"slug":106,"title":121},"AI for public consultation response analysis",{"title":123,"useCases":124,"organization":126,"vendors":127,"summary":129,"stage":25,"year":130,"channels":131,"languages":132,"metrics":133,"outcomeDisclosed":13,"sources":142,"verification":145,"grade":40,"id":146,"useCaseTitles":147},"CDC: MedCoder codes causes of death on death certificates to ICD-10",[125],"medical-coding-automation",{"name":6,"anonymized":14,"country":7,"region":8,"industry":9,"wikidata":10},[128],{"name":6,"role":23},"MedCoder uses natural language processing to clean the literal text of causes of death on death certificates and to assign ICD-10 codes. It flags complex or frequently miscoded cases for manual review. CDC says the share of deaths coded automatically and accurately rose, which speeds up mortality surveillance.",2022,[28],[30],[134],{"kpi":135,"value":136,"unit":137,"qualifier":138,"period":139,"baseline":140,"claimant":64,"quote":141,"sourceUrl":34},"automation-rate",85,"percent","at-least","share of deaths coded automatically and accurately","70-75% before MedCoder","MedCoder increased the percentage of deaths that can be automatically and accurately coded from 70-75% to over 85%, resulting in substantial cost savings (hundreds of thousands of dollars) and significantly enhancing the timeliness of data for urgent public health concerns (e.g., COVID, drug overdose deaths), enabling near real-time surveillance.",[143],{"url":34,"title":144,"publisher":36},"2025 Federal AI use case inventory, HHS entry: MedCoder",{"level":38,"checkedAt":39},"cdc-medcoder-cause-of-death-coding",[148],{"slug":125,"title":149},"AI medical coding for clinical encounters",{"title":151,"useCases":152,"organization":154,"vendors":155,"summary":157,"stage":25,"year":130,"channels":158,"languages":159,"metrics":160,"outcomeDisclosed":13,"sources":171,"verification":174,"grade":40,"id":175,"useCaseTitles":176},"CDC: School Closure Awareness System using a large language model",[153],"epidemic-intelligence-signal-detection",{"name":6,"anonymized":14,"country":7,"region":8,"industry":9,"wikidata":10},[156],{"name":6,"role":23},"CDC uses a large language model to read Facebook posts from about 40,000 school and district accounts and to classify unplanned school closures by event type and status change. Staff review and recode the output every 24 hours. It replaced a costly manual process done through contractors.",[28],[30],[161,168],{"kpi":162,"value":163,"unit":164,"currency":165,"qualifier":62,"period":166,"claimant":64,"quote":167,"sourceUrl":34},"cost-savings",2000000,"currency","USD","to date, as stated in the 2025 inventory","The AI system has saved nearly $2 million in contracting fees and reduced human work hours by 200 hours.",{"kpi":59,"value":169,"unit":61,"qualifier":170,"period":166,"claimant":64,"quote":167,"sourceUrl":34},200,"exact",[172],{"url":34,"title":173,"publisher":36},"2025 Federal AI use case inventory, HHS entry: The School Closure Awareness System",{"level":38,"checkedAt":39},"cdc-school-closure-awareness-system",[177],{"slug":153,"title":178},"AI epidemic intelligence for detecting public health threat signals in open source news and social media",1794572865806]