[{"data":1,"prerenderedAt":619},["ShallowReactive",2],{"uc-radiology-report-drafting-assistant":3,"uc-regulations":394},{"useCase":4,"evidence":191,"blitsAiDeployments":276,"benchmarks":277,"indicative":289,"related":292,"indexability":392,"includeUnpublished":197},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":24,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"problem":30,"problemStats":31,"howItWorks":32,"valueDrivers":33,"kpis":36,"indicativeValue":41,"macroEstimates":84,"feasibility":85,"implementation":98,"risk":136,"blitsAi":168,"faq":170,"related":183,"datePublished":186,"dateModified":186,"lastVerified":186,"changelog":187,"slug":190},"AI drafting of radiology reports for radiologist review","Radiology report drafting","AI radiology report drafting software","AI writes a first draft of the radiology report for the radiologist to edit and sign. Northwestern Medicine's own model cut documentation time by 15.5%.","published","AI radiology report drafting is software that writes a first draft of the report from the image and the clinical data, which the radiologist edits and signs. The draft comes from a vision language model, either trained on the organization's own signed reports or built on a radiology foundation model, and it replaces the blank template or dictation at the start of the read. The radiologist stays responsible for the final interpretation.",[12,13,14,15],"AI radiology report generation","generative AI radiograph reporting","draft radiology reporting","vision language model radiology reports",[17],"healthcare",[19,20],"operations","knowledge-management",[22,23],"content-generation","computer-vision",[25],"internal-tools","employee-facing","copilot","emerging","diagnostic imaging","For a routine radiograph, the radiologist reads the image and then dictates or types the report\nfrom scratch or from a predefined template. The authors of the Northwestern Medicine study\ndescribe an AI draft as a starting point, much as an attending radiologist verifies and edits a\ntrainee report. Radiology Partners, the largest radiology practice in the United States,\ndescribes the pressure as rising imaging demand and a growing capacity gap, with worklists that\nkeep getting longer.\n\nDocumentation is one of the few parts of the read that can be shortened without changing who\ndecides. Northwestern's authors add that faster documentation may ease shortfalls in imaging\naccess and reduce burnout. The risk sits in the same place: a draft that reads well can pass\nreview with an error in it, so the workflow has to keep the radiologist's judgment in charge.",[],"1. **Send the image and context.** When a radiograph is acquired, an inference server receives\n   the image together with clinical data from the EHR, such as the reason for examination, the\n   procedure type, the comparison study and the name of the reading radiologist.\n2. **Generate the draft.** A multimodal model writes free text report language. Northwestern\n   Medicine pairs a vision transformer image encoder with a text decoder of 125 million\n   parameters and trained it on its own institutional dataset. Radiology Partners applies a\n   multimodal foundation model from Cognita Imaging.\n3. **Place the draft in the reporting software.** The draft appears in the fields of the\n   radiologist's normal reporting tool, so there is no second screen to open. Northwestern\n   integrated it with Epic and PowerScribe.\n4. **Review, edit and sign.** The radiologist checks the draft against the image, edits it and\n   signs. No report goes out before that step.\n5. **Check for urgent findings.** Because the draft describes severity and chronicity in text, the\n   same output can be scanned for findings that need action. Northwestern screened 97,651\n   radiographs for unexpected pneumothorax this way and reported a sensitivity of 72.7% and a\n   specificity of 99.9%.\n6. **Log and sample.** Every draft, edit and signature is logged, and a sample of final reports\n   goes to peer review against a rating scale for clinical accuracy and text quality.",[34,35],"employee-productivity","speed",[37,38,39,40],"productivity-gain","hours-saved","time-saved-per-task","accuracy",{"referenceOrg":42,"inputs":43,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A health system with 12 hospitals reading about 700,000 plain radiographs a year",[44,51,58,65,72],{"key":45,"label":46,"low":47,"high":48,"unit":49,"note":50},"radiographs","Plain radiographs reported per year",400000,700000,"radiographs per year","Editorial assumption. The Northwestern Medicine study datasets were derived from 299,164 radiographs between November 2023 and April 2024, so the high value is a full year of a health system of that size. Replace with your own volume.",{"key":52,"label":53,"low":54,"high":55,"unit":56,"note":57},"shareDrafted","Share of radiographs where a draft is used",0.5,0.9,"fraction of radiographs","Editorial assumption for the exam types and sites you enable first. Replace with the share your pilot reaches.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"secondsPerReport","Documentation time per radiograph without a draft",150,190,"seconds","The Northwestern Medicine study measured a mean of 189.2 seconds for radiographs reported without the model. The low value assumes a faster department. Replace with your own timestamps.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"gain","Documentation time saved when a draft is used",0.05,0.155,"fraction of documentation time","The high value is the 15.5% documentation efficiency gain reported by Northwestern Medicine in a nonrandomized study. The low value is an editorial haircut for a first deployment.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"costPerHour","Fully loaded radiologist cost per hour",200,350,"USD per hour","Editorial assumption, replace with your own cost of radiologist time.","radiographs * shareDrafted * secondsPerReport * gain \u002F 3600 * costPerHour","USD","per year","Radiologist time freed, valued at the cost of that time","A value of time, not cash, and only if the freed minutes are used for more reads or less overtime. It leaves out the cost of the model, the integration and the peer review, any gain from catching urgent findings sooner, and the added reading effort when a draft is poor.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":92},"high","The model needs a paired archive of images and signed reports from your own department, or a foundation model that has been validated on your scanners and patient mix. The draft then has to appear inside the reporting software and the EHR, and the department needs a peer review process and a regulatory position before it scales.",[89,90,91],"A large archive of radiographs with signed reports and the clinical context entered with each order","A peer review rating scale and a pool of radiologists who can score drafts and final reports","Time stamps for documentation time with and without a draft",[93,94,95,96,97],"Picture archiving and communication system (PACS) or the imaging archive","Reporting software and radiology information system","Electronic health record, for the reason for examination and prior studies","Peer review or quality assurance tool","Identity and access management for the inference service",{"steps":99,"guardrails":115,"humanInTheLoop":120,"kpisToInstrument":121,"failureModes":126},[100,103,106,109,112],{"title":101,"detail":102},"Start with one exam type and one site","Pick a high volume exam such as a chest radiograph, where findings follow familiar patterns and a radiologist can judge a draft quickly. Leave cross sectional imaging for a later wave.",{"title":104,"detail":105},"Build the dataset and the rating scale first","Pair images with signed reports, split them by patient so the same person never appears in training and evaluation, and agree on a rating scale for clinical accuracy and text quality before the first draft is written.",{"title":107,"detail":108},"Put the draft where the radiologist already works","Insert it into the fields of the existing reporting tool as editable text. A separate window that has to be opened will be ignored on a busy shift.",{"title":110,"detail":111},"Run a prospective comparison","Measure documentation time on studies with and without a draft in live reads, sample the final reports for peer review, and log every draft and edit. Northwestern's study used this design.",{"title":113,"detail":114},"Settle the regulatory path before you scale","Ask the device regulator whether the drafting software counts as a medical device in your market. Radiology Partners said in July 2025 that its tool ran under Institutional Review Board protocols while it pursued FDA approval.",[116,117,118,119],"The radiologist verifies, edits and signs every report; a draft is never released to a referring clinician on its own","Drafts are enabled only for the exam types and sites that were validated","Every draft, edit and signature is logged and traceable to the model version","A fallback to the normal template when the model is down or returns nothing","The radiologist owns the final interpretation and signs the report. A named clinical lead owns model performance, reviews the peer review sample every month and decides which exam types and sites are added.",[122,123,124,125],"Documentation time per study with a draft against without one, by radiologist and exam type","Peer review score for clinical accuracy and text quality on final reports, with and without a draft","Share of drafts accepted with little editing, and the types of error radiologists correct","Sensitivity and specificity of any urgent finding flag against the signed report",[127,130,133],{"title":128,"detail":129},"Automation bias","A fluent draft passes review with an error in it, because the radiologist reads the text before the image. Sample final reports for peer review and track the error types.",{"title":131,"detail":132},"Performance drift on new equipment or populations","The model was trained on one department's scanners and patient mix and degrades elsewhere without any visible signal. Validate on local data and monitor accuracy by site.",{"title":134,"detail":135},"Drafts that are ignored","Radiologists dictate over the draft because it needs too much editing, so no time is saved. Track acceptance and edit distance and retrain on the corrected reports.",{"euAiAct":137,"regulations":139,"guidance":145,"controls":162,"incidents":167},{"tier":86,"basis":138},"Software that creates diagnostic report text from a medical image is likely to be medical device software under the EU Medical Device Regulation. A product that needs third party conformity assessment under that regulation is high risk through Article 6(1) and Annex I of the EU AI Act, whether or not it appears in Annex III. That Annex I route applies from 2 August 2028 (Article 113(c)). Until then Article 4 (AI literacy) and Article 5 (prohibited practices) already bind the hospital as a deployer. The radiologist's signed interpretation stays a human decision.",[140,141,142,143,144],"eu-ai-act","gdpr","hipaa","iso-42001","nist-ai-rmf",[146,152,156],{"title":147,"issuer":148,"region":149,"url":150,"note":151},"Regulation (EU) 2017\u002F745 on medical devices","European Union","europe","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2017\u002F745\u002Foj","Software that provides information used for diagnostic decisions can be a medical device, and its risk class decides whether a notified body must assess it before CE marking.",{"title":153,"issuer":148,"region":149,"url":154,"note":155},"Article 6: Classification Rules for High-Risk AI Systems","https:\u002F\u002Fartificialintelligenceact.eu\u002Farticle\u002F6\u002F","A product that is itself a medical device requiring third party conformity assessment, or a safety component of one, is high risk under the EU AI Act.",{"title":157,"issuer":158,"region":159,"url":160,"note":161},"List of Artificial Intelligence-Enabled Medical Devices","US Food and Drug Administration","north-america","https:\u002F\u002Fwww.fda.gov\u002Fmedical-devices\u002Fsoftware-medical-device-samd\u002Fartificial-intelligence-and-machine-learning-aiml-enabled-medical-devices","FDA's list of AI enabled devices authorized for marketing in the United States. Check it, and the device's cleared indications, before relying on a drafting tool outside a research protocol.",[163,164,165,166],"Documented intended use and the list of exam types and sites the model is validated for","Radiologist signature on every report, with the draft and the edits kept in the audit trail","Monthly peer review sample and performance report by site and exam type","Model version control and a change process that includes revalidation on local data",[],{"howToBuild":169},"Blits.ai does not read radiographs, and the model that turns an image into draft text stays\nin the imaging stack. What Blits.ai can run is the text side around it. An **AI agent** can\ntake the draft and the clinical context from the draft service through a **custom function** (a\nREST call to the reporting system), restructure it into the department's template with\nprompts and a **knowledge base** of the reporting guidelines and macros, and return it for the\nradiologist to edit. A **human in the loop approval** step keeps the signature with the\nradiologist, and the agent can use a model the hospital hosts itself, through the Hugging Face or\nOpenAI compatible connection, with **EU or UAE data residency** and **PII masking** at the\ngateway.\n\n**Test suites** replay a fixed set of de identified draft and report pairs on every prompt\nor model change, **monitors** check the agent on a schedule and alert by email or webhook, and\n**analytics** with full execution traces show how often drafts are returned and edited. Blits.ai\ndoes not clear the underlying device for medical use; that stays with the vendor or the\nhospital that builds it.",[171,174,177,180],{"question":172,"answer":173},"How much time does AI report drafting save?","In a prospective cohort study of 11,980 model assisted radiograph interpretations at Northwestern Medicine, documentation efficiency rose by 15.5% and the authors calculated a net saving of over 63 documentation hours across the study period. The study was nonrandomized and covered one health system, so treat it as a first measurement rather than a benchmark.",{"question":175,"answer":176},"Does the AI replace the radiologist?","No. In both deployments on this page the radiologist reviews, edits and signs the report. Northwestern's peer review of 800 studies found no difference in clinical accuracy or text quality between model assisted and other reports.",{"question":178,"answer":179},"Should we train our own model or use a foundation model?","Both routes appear in the evidence. Northwestern Medicine trained a small model on its own institutional reports. Radiology Partners applies a multimodal foundation model that it acquired with Cognita Imaging. A model trained on your own data fits your report style, but it needs a large paired archive and a validation effort; a foundation model needs validation on your scanners and patients before use.",{"question":181,"answer":182},"Is a report drafting tool a medical device?","Often yes. Software that produces diagnostic text from an image is likely to count as medical device software in the EU and the United States, which is why Radiology Partners described its drafting tool as running under Institutional Review Board protocols while it pursued FDA approval. Ask your regulator before moving beyond a research protocol.",[184,185],"radiology-worklist-triage","ambient-clinical-documentation","2026-10-14",[188],{"date":186,"note":189},"First published","radiology-report-drafting-assistant",[192,231],{"title":193,"useCases":194,"organization":195,"vendors":199,"summary":206,"stage":207,"year":208,"channels":209,"languages":210,"metrics":212,"outcomeDisclosed":197,"sources":213,"verification":226,"grade":228,"id":229,"organizationSlug":230},"Radiology Partners: Mosaic Drafting pre drafts x-ray reports",[190],{"name":196,"anonymized":197,"country":198,"region":159,"industry":17},"Radiology Partners",false,"US",[200,203],{"name":201,"role":202},"Cognita Imaging","model-provider",{"name":204,"role":205},"Amazon Web Services","platform","Radiology Partners, the largest radiology practice in the United States, built Mosaic Drafting inside its Mosaic Clinical Technologies division. The tool applies a multimodal foundation model to pre draft x-ray reports (later also head CT) for a radiologist to review, edit and sign. The model comes from Cognita Imaging, which Radiology Partners acquired. In July 2025 the company said Mosaic Drafting was being implemented under Institutional Review Board protocols while it pursued FDA approval, so the record is a pilot. No outcome from live use is published on the sources cited here.","pilot",2025,[25],[211],"en",[],[214,218,221],{"url":215,"title":216,"publisher":196,"date":217},"https:\u002F\u002Fwww.radpartners.com\u002F2025\u002F07\u002Fradiology-partners-unveils-mosaic-clinical-technologies-and-mosaicos\u002F","Radiology Partners Unveils Mosaic Clinical Technologies and MosaicOS","2025-07-15",{"url":219,"title":220,"publisher":196},"https:\u002F\u002Fwww.radpartners.com\u002F2025\u002F11\u002Fseventeen-radiology-partners-physicians-and-experts-participating-in-multiple-rsna-2025-sessions\u002F","Seventeen Radiology Partners physicians and experts participating in multiple RSNA 2025 sessions",{"url":222,"title":223,"publisher":224,"date":225},"https:\u002F\u002Fwww.statnews.com\u002F2025\u002F12\u002F01\u002Fcognita-imaging-radiology-partners-what-next-vision-language-models\u002F","Cognita CEO on next steps for vision language models in radiology","STAT","2025-12-01",{"level":227,"checkedAt":186},"source-verified","B","radiology-partners-mosaic-drafting",null,{"title":232,"useCases":233,"organization":234,"vendors":237,"summary":244,"stage":245,"year":246,"channels":247,"languages":248,"metrics":249,"outcomeDisclosed":264,"sources":265,"verification":274,"grade":228,"id":275,"organizationSlug":230},"Northwestern Medicine: in house generative model that drafts radiograph reports",[190],{"name":235,"anonymized":197,"country":198,"region":159,"industry":17,"wikidata":236},"Northwestern Medicine","Q50036890",[238,240,242],{"name":235,"role":239},"in-house",{"name":241,"role":205},"Epic Systems",{"name":243,"role":205},"Nuance Communications (PowerScribe)","Northwestern Medicine trained its own multimodal generative model on its institutional radiology data. For a plain radiograph the model writes a draft report from the image and clinical data such as the reason for examination, and the draft appears in the radiologist's reporting software, where the radiologist verifies and edits it. The model ran across the 12 hospital health system from November 2023 to April 2024. In a prospective cohort study, drafts were associated with a 15.5% documentation efficiency gain, and peer review of 800 studies found no difference in clinical accuracy or textual quality.","production",2024,[25],[211],[250,259],{"kpi":37,"value":251,"unit":252,"qualifier":253,"period":254,"baseline":255,"claimant":256,"quote":257,"sourceUrl":258},15.5,"percent","exact","November 15, 2023 to April 24, 2024, radiograph reports","Documentation time for radiographs read without the model","organization","We found a 15.5% documentation efficiency benefit with no decrease in clinical accuracy on peer review, representing a net time savings of over 63 documentation hours over the study period, or a reduction from roughly 79 to 67 radiologist shifts required to provide coverage.","https:\u002F\u002Fwww.ebi.ac.uk\u002Feuropepmc\u002Fwebservices\u002Frest\u002FPMC12142447\u002FfullTextXML",{"kpi":38,"value":260,"unit":261,"qualifier":262,"period":263,"claimant":256,"quote":257,"sourceUrl":258},63,"hours","at-least","over the study period, November 15, 2023 to April 24, 2024",true,[266,271],{"url":267,"title":268,"publisher":269,"date":270},"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC12142447\u002F","Efficiency and Quality of Generative AI Assisted Radiograph Reporting","JAMA Network Open (PubMed Central)","2025-06-02",{"url":258,"title":272,"publisher":273,"date":270},"Efficiency and Quality of Generative AI Assisted Radiograph Reporting (Europe PMC full text)","Europe PMC",{"level":227,"checkedAt":186},"northwestern-medicine-generative-ai-radiograph-reporting",0,[278,284],{"kpi":38,"label":279,"unit":261,"aggregate":197,"higherIsBetter":264,"n":280,"nUpTo":276,"median":260,"min":260,"max":260,"byClaimant":281,"vendorOnly":197,"points":282},"Hours saved",1,{"organization":280,"vendor":276,"regulator":276,"independent":276},[283],{"evidenceId":275,"organization":235,"value":260,"qualifier":262,"claimant":256,"grade":228,"pooled":264},{"kpi":37,"label":285,"unit":252,"aggregate":264,"higherIsBetter":264,"n":280,"nUpTo":276,"median":251,"min":251,"max":251,"byClaimant":286,"vendorOnly":197,"points":287},"Productivity gain",{"organization":280,"vendor":276,"regulator":276,"independent":276},[288],{"evidenceId":275,"organization":235,"value":251,"qualifier":253,"claimant":256,"grade":228,"pooled":264},{"low":290,"high":291},83333.33333333334,1803812.5,[293,327,356,378],{"slug":184,"title":294,"shortTitle":295,"definition":296,"status":9,"industries":297,"functions":298,"patterns":299,"audience":26,"autonomy":302,"adoptionStage":303,"segment":304,"evidenceCount":305,"publicEvidenceCount":305,"organizations":306,"bestGrade":228,"headline":319,"lastVerified":326,"indexable":264},"AI prioritization of radiology and imaging worklists","Radiology worklist triage","AI radiology worklist triage analyzes a medical image right after a scan and flags time sensitive findings, so the most urgent cases are read and acted on first. Examples include a brain bleed, a stroke causing large vessel occlusion or a pulmonary embolism. It reorders the radiologist's worklist and notifies the care team, and a radiologist confirms every finding before it changes a patient's treatment.",[17],[19],[23,300,301],"classification-and-routing","anomaly-detection","assist","mainstream","emergency and inpatient imaging",12,[307,308,309,310,311,312,313,314,315,316,317,318],"Adventist Health + Rideout","Isala","M42","Mercy","Ochsner Health","Seha Virtual Hospital (Saudi Ministry of Health)","Sheba Medical Center","Sidra Medicine","Singapore General Hospital (SGH)","Temple Health","UPMC Hamot","WellSpan Health",{"kpi":320,"label":321,"unit":252,"n":280,"nUpTo":276,"kind":322,"value":323,"qualifier":324,"claimant":325,"organization":307,"vendorReported":264},"processing-time-reduction","Cycle time reduction","reported",44,"approximately","vendor","2026-09-29",{"slug":185,"title":328,"shortTitle":329,"definition":330,"status":9,"industries":331,"functions":332,"patterns":333,"audience":26,"autonomy":27,"adoptionStage":303,"evidenceCount":336,"publicEvidenceCount":336,"organizations":337,"bestGrade":228,"headline":349,"lastVerified":355,"indexable":264},"AI ambient scribe for clinical documentation","Ambient clinical documentation","Ambient clinical documentation is an AI scribe that listens, with the patient's consent, to the conversation between a clinician and a patient and drafts the clinical note. It often also drafts the letter or after visit summary, for the clinician to review, edit and sign in the health record; it documents and does not diagnose or decide on treatment.",[17],[19,20],[334,335,22],"speech-analytics","summarization",11,[338,339,340,341,342,343,344,345,346,347,348],"Beth Israel Lahey Health","BJC Health","Capio","Charité Universitätsmedizin Berlin","Great Ormond Street Hospital for Children NHS Foundation Trust","Kaiser Permanente","Mount Sinai Medical Center","PureHealth","Reid Health","Sutter Health","US Department of Veterans Affairs, Veterans Health Administration",{"kpi":350,"label":351,"unit":252,"n":352,"nUpTo":276,"kind":353,"value":354,"qualifier":253,"claimant":230,"organization":230,"vendorReported":197},"handling-time-reduction","Handling time reduction",4,"median",22.5,"2026-09-27",{"slug":357,"title":358,"shortTitle":359,"definition":360,"status":9,"industries":361,"functions":363,"patterns":364,"audience":26,"autonomy":27,"adoptionStage":367,"segment":368,"evidenceCount":369,"publicEvidenceCount":369,"organizations":370,"bestGrade":228,"headline":374,"lastVerified":377,"indexable":264},"teacher-lesson-planning-and-resource-assistant","AI assistant for teachers: lesson planning and classroom resources","Teacher lesson planning assistant","An AI lesson planning assistant is software that drafts lesson plans, slides, quizzes and worksheets from a teacher's learning goal, which the teacher edits and checks before using in class. It works from the school's curriculum and leaves teaching, marking and every pupil facing decision to the teacher.",[362],"education",[19,20],[22,365,366],"rag-knowledge-assistant","conversational-agent","early-adopters","schools",3,[371,372,373],"NSW Department of Education","Oak National Academy","Ministry of Education, Singapore",{"kpi":37,"label":285,"unit":252,"n":280,"nUpTo":276,"kind":322,"value":375,"qualifier":253,"claimant":376,"organization":372,"vendorReported":197},24,"independent","2026-10-13",{"slug":379,"title":380,"shortTitle":381,"definition":382,"status":9,"industries":383,"functions":385,"patterns":386,"audience":26,"autonomy":27,"adoptionStage":367,"evidenceCount":352,"publicEvidenceCount":352,"organizations":387,"bestGrade":228,"headline":230,"lastVerified":377,"indexable":264},"newsroom-drafting-and-article-summaries","AI drafting and article summaries for newsrooms with editor review","Newsroom drafting and summaries","Newsroom AI turns a reporter's verified notes into a draft story and long articles into key point summaries, which a journalist edits before anything is published. Publishers use it for routine stories such as community news and event listings, and for summaries at the top of articles.",[384],"media-and-entertainment",[19,20],[22,335],[388,389,390,391],"Aftonbladet","BBC","Newsquest","Gannett",{"indexable":264,"reasons":393},[],[395,400,405,412,418,425,431,438,446,453,459,465,471,477,484,490,496,502,508,515,520,525,531,537,543,548,553,559,566,571,579,586,591,597,603,608,613],{"id":140,"label":396,"issuer":148,"region":149,"url":397,"description":398,"useCases":399,"indexable":264},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",295,{"id":141,"label":401,"issuer":148,"region":149,"url":402,"description":403,"useCases":404,"indexable":264},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",265,{"id":143,"label":406,"issuer":407,"region":408,"url":409,"description":410,"useCases":411,"indexable":264},"ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",148,{"id":144,"label":413,"issuer":414,"region":159,"url":415,"description":416,"useCases":417,"indexable":264},"NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",115,{"id":419,"label":420,"issuer":421,"region":149,"url":422,"description":423,"useCases":424,"indexable":264},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",84,{"id":426,"label":427,"issuer":148,"region":149,"url":428,"description":429,"useCases":430,"indexable":264},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",73,{"id":432,"label":433,"issuer":434,"region":149,"url":435,"description":436,"useCases":437,"indexable":264},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",53,{"id":439,"label":440,"issuer":441,"region":442,"url":443,"description":444,"useCases":445,"indexable":264},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":447,"label":448,"issuer":449,"region":442,"url":450,"description":451,"useCases":452,"indexable":264},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":454,"label":455,"issuer":148,"region":149,"url":456,"description":457,"useCases":458,"indexable":264},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",23,{"id":460,"label":461,"issuer":462,"region":408,"url":463,"description":464,"useCases":458,"indexable":264},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":466,"label":467,"issuer":468,"region":159,"url":469,"description":470,"useCases":458,"indexable":264},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":142,"label":472,"issuer":473,"region":159,"url":474,"description":475,"useCases":476,"indexable":264},"HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",20,{"id":478,"label":479,"issuer":480,"region":149,"url":481,"description":482,"useCases":483,"indexable":264},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":485,"label":486,"issuer":148,"region":149,"url":487,"description":488,"useCases":489,"indexable":264},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",15,{"id":491,"label":492,"issuer":493,"region":408,"url":494,"description":495,"useCases":489,"indexable":264},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":497,"label":498,"issuer":499,"region":159,"url":500,"description":501,"useCases":489,"indexable":264},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":503,"label":504,"issuer":148,"region":149,"url":505,"description":506,"useCases":507,"indexable":264},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":509,"label":510,"issuer":511,"region":159,"url":512,"description":513,"useCases":514,"indexable":264},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":516,"label":517,"issuer":148,"region":149,"url":518,"description":519,"useCases":514,"indexable":264},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":521,"label":522,"issuer":148,"region":149,"url":523,"description":524,"useCases":514,"indexable":264},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":526,"label":527,"issuer":528,"region":408,"url":529,"description":530,"useCases":514,"indexable":264},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":532,"label":533,"issuer":534,"region":159,"url":535,"description":536,"useCases":305,"indexable":264},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":538,"label":539,"issuer":540,"region":149,"url":541,"description":542,"useCases":336,"indexable":264},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",{"id":544,"label":545,"issuer":148,"region":149,"url":546,"description":547,"useCases":336,"indexable":264},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":549,"label":550,"issuer":148,"region":149,"url":551,"description":552,"useCases":336,"indexable":264},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":554,"label":555,"issuer":441,"region":442,"url":556,"description":557,"useCases":558,"indexable":264},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":560,"label":561,"issuer":562,"region":159,"url":563,"description":564,"useCases":565,"indexable":264},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",8,{"id":567,"label":568,"issuer":148,"region":149,"url":569,"description":570,"useCases":565,"indexable":264},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":572,"label":573,"issuer":574,"region":575,"url":576,"description":577,"useCases":578,"indexable":264},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",6,{"id":580,"label":581,"issuer":582,"region":149,"url":583,"description":584,"useCases":585,"indexable":264},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",5,{"id":587,"label":588,"issuer":148,"region":149,"url":589,"description":590,"useCases":352,"indexable":264},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":592,"label":593,"issuer":594,"region":149,"url":595,"description":596,"useCases":352,"indexable":264},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":598,"label":599,"issuer":600,"region":442,"url":601,"description":602,"useCases":369,"indexable":264},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":604,"label":605,"issuer":148,"region":149,"url":606,"description":607,"useCases":369,"indexable":264},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":609,"label":610,"issuer":441,"region":442,"url":611,"description":612,"useCases":369,"indexable":264},"mas-shared-responsibility-framework","MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":614,"label":615,"issuer":616,"region":159,"url":617,"description":618,"useCases":369,"indexable":264},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1792049860862]