[{"data":1,"prerenderedAt":139},["ShallowReactive",2],{"uc-org-u-s-food-and-drug-administration":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},"u-s-food-and-drug-administration","U.S. Food and Drug Administration","US","north-america","government","Q204711",5,3,true,false,[16,50,77,98,118],{"title":17,"useCases":18,"organization":20,"vendors":21,"summary":22,"stage":23,"year":24,"channels":25,"languages":27,"metrics":29,"outcomeDisclosed":13,"sources":38,"verification":42,"grade":45,"id":46,"useCaseTitles":47},"FDA: AI assisted redaction of confidential commercial information in FOIA responses",[19],"freedom-of-information-request-processing",{"name":6,"anonymized":14,"country":7,"region":8,"industry":9,"wikidata":10},[],"At the 2026 Food and Drug Law Institute annual conference, the director of the FDA's Office of Management and Enterprise Services described how the agency uses AI for FOIA processing, including automated redaction of confidential commercial information. Officials said the AI assisted redaction has reached an accuracy of 98%. The agency's Elsa tool also supports FOIA decisions, such as checking whether a sponsor has acknowledged an application or whether a requester is a first or third party. The talk does not say how accuracy was measured or how many records it covers.","production",2026,[26],"internal-tools",[28],"en",[30],{"kpi":31,"value":32,"unit":33,"qualifier":34,"claimant":35,"quote":36,"sourceUrl":37},"accuracy",98,"percent","exact","organization","Officials reported that AI-assisted redaction has achieved accuracy rates of 98%.","https:\u002F\u002Fwww.skadden.com\u002Finsights\u002Fpublications\u002F2026\u002F05\u002Freport-from-fdli-annual-meeting",[39],{"url":37,"title":40,"publisher":41},"Report From FDLI Annual Meeting: FDA's Expanding Use of AI, What Regulated Industry Should Know","Skadden, Arps, Slate, Meagher & Flom",{"level":43,"checkedAt":44},"source-verified","2026-10-13","C","fda-ai-assisted-foia-redaction",[48],{"slug":19,"title":49},"AI for freedom of information request processing",{"title":51,"useCases":52,"organization":54,"vendors":56,"summary":60,"stage":23,"year":61,"channels":62,"languages":63,"metrics":64,"outcomeDisclosed":14,"sources":65,"verification":70,"grade":72,"id":73,"useCaseTitles":74},"US Food and Drug Administration: AI extraction of data from IND safety reports into FAERS",[53],"adverse-event-case-intake",{"name":55,"anonymized":14,"country":7,"region":8,"industry":9},"U.S. Food and Drug Administration, Center for Drug Evaluation and Research",[57],{"name":58,"role":59},"ThinkTrends","platform","FDA's Center for Drug Evaluation and Research reports in the 2025 federal AI use case inventory that it uses OCR and AI, through the commercial tool ThinkTrends, to extract data from the Investigational New Drug safety reports that sponsors send in. The extracted data is converted to the E2B(R2) format and ingested automatically into the FDA Adverse Event Reporting System. The inventory describes manual extraction of these reports as labor intensive and time consuming for regulatory staff, and states the aim as faster processing and regulatory action on adverse events reported in clinical trials. It lists the use as deployed since March 2025.",2025,[26],[28],[],[66],{"url":67,"title":68,"publisher":69},"https:\u002F\u002Fraw.githubusercontent.com\u002Fombegov\u002F2025-Federal-Agency-AI-Use-Case-Inventory\u002Fmain\u002FData\u002F2025_individually_reported_AI_use_cases.csv","2025 federal agency AI use case inventory, individually reported use cases (raw data)","Office of Management and Budget (GitHub)",{"level":43,"checkedAt":71},"2026-09-27","B","fda-ind-safety-report-extraction",[75],{"slug":53,"title":76},"AI for pharmacovigilance adverse event case intake",{"title":78,"useCases":79,"organization":80,"vendors":81,"summary":87,"stage":23,"year":61,"channels":88,"languages":89,"metrics":90,"outcomeDisclosed":14,"sources":91,"verification":93,"grade":72,"id":95,"useCaseTitles":96},"US Food and Drug Administration: FRED tool that proposes FOIA redactions",[19],{"name":55,"anonymized":14,"country":7,"region":8,"industry":9},[82,84],{"name":6,"role":83},"in-house",{"name":85,"role":86},"Contractor teams (not named)","integrator","FDA's Center for Drug Evaluation and Research uses the FOIA Redaction (FRED) tool, a generative AI system built by FDA and contractor teams, to help FOIA staff redact records more efficiently and consistently, because redaction is time consuming and FOIA backlogs build up. The tool returns a PDF with boxes around the text it recommends redacting, each with a comment giving the redaction code. Its data are completed FDA Form 483 inspection records in their original and staff redacted versions, and every output needs human review and approval. Listed as deployed since May 2025; no outcome figures are published.",[26],[28],[],[92],{"url":67,"title":68,"publisher":69},{"level":43,"checkedAt":94},"2026-09-26","fda-foia-redaction-tool",[97],{"slug":19,"title":49},{"title":99,"useCases":100,"organization":101,"vendors":102,"summary":105,"stage":23,"year":106,"channels":107,"languages":110,"metrics":111,"outcomeDisclosed":14,"sources":112,"verification":114,"grade":72,"id":115,"useCaseTitles":116},"US Food and Drug Administration: chatbot for adverse event and product problem reports on the Safety Reporting Portal",[53],{"name":6,"anonymized":14,"country":7,"region":8,"industry":9,"wikidata":10},[103],{"name":104,"role":59},"Druid AI","FDA reports in the 2025 federal AI use case inventory that a conversational assistant built on the commercial platform Druid helps people who report an adverse event or product problem through the Safety Reporting Portal. It answers questions from a knowledge base, routes the reporter to the right form for the product type, helps complete it and submits the report to the portal through an API, with the stated aims of better data integrity and faster form completion. The inventory lists it as deployed since March 2024; no measured results are published.",2024,[108,109],"web-chat","api",[28],[],[113],{"url":67,"title":68,"publisher":69},{"level":43,"checkedAt":71},"fda-safety-reporting-portal-chatbot",[117],{"slug":53,"title":76},{"title":119,"useCases":120,"organization":122,"vendors":124,"summary":126,"stage":23,"year":127,"channels":128,"languages":129,"metrics":130,"outcomeDisclosed":14,"sources":131,"verification":133,"grade":72,"id":135,"useCaseTitles":136},"US Food and Drug Administration: risk based prioritization of import filer evaluations",[121],"inspection-prioritization",{"name":123,"anonymized":14,"country":7,"region":8,"industry":9},"U.S. Food and Drug Administration, Office of Information Operations",[125],{"name":6,"role":83},"FDA's Office of Information Operations reports in the 2025 federal AI use case inventory a machine learning model that scores the more than 4,000 filers in the FDA's import filer inventory (brokers and other parties who submit import entries to the agency) by risk, so staff can prioritize which filers to evaluate instead of sorting through the population manually. The output is a list of every filer with a relative risk score for staff to use when planning evaluations, shown on an interactive dashboard. FDA lists it as deployed since January 2023; no outcome figures are published.",2023,[26],[28],[],[132],{"url":67,"title":68,"publisher":69},{"level":43,"checkedAt":134},"2026-09-29","fda-filer-evaluation-risk-prioritization",[137],{"slug":121,"title":138},"AI for risk based inspection prioritization in food safety, workplace and environmental regulation",1794503904300]