[{"data":1,"prerenderedAt":553},["ShallowReactive",2],{"uc-radiology-worklist-triage":3,"uc-regulations":345},{"useCase":4,"evidence":175,"blitsAiDeployments":237,"benchmarks":238,"indicative":245,"related":248,"indexability":343,"includeUnpublished":181},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":20,"channels":24,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"problem":30,"problemStats":31,"howItWorks":32,"valueDrivers":33,"kpis":37,"indicativeValue":41,"macroEstimates":76,"feasibility":77,"implementation":89,"risk":122,"blitsAi":154,"faq":156,"related":169,"datePublished":170,"dateModified":170,"lastVerified":170,"changelog":171,"slug":174},"AI prioritization of radiology and imaging worklists","Radiology worklist triage","AI radiology worklist triage software","AI flags urgent CT and MRI findings and reorders the radiologist worklist. A regional stroke center cut transfer time 44% with a program that included Viz.ai.","published","An AI system that analyzes a medical image immediately after a scan, flags time sensitive findings such as a brain bleed, a stroke causing large vessel occlusion or a pulmonary embolism, and reorders the radiologist's worklist and notifies the care team so the most urgent cases are read and acted on first, while a radiologist confirms every finding before it changes a patient's treatment.",[12,13,14,15],"AI radiology triage","imaging worklist prioritization","critical findings AI","AI stroke detection",[17],"healthcare",[19],"operations",[21,22,23],"computer-vision","classification-and-routing","anomaly-detection",[25],"internal-tools","employee-facing","assist","mainstream","emergency and inpatient imaging","Within the same priority class, a radiology worklist is normally read in roughly the order scans\narrive. A scan that shows a brain bleed or a blood clot blocking a major vessel can sit behind\nseveral routine studies at the same priority level before a radiologist opens it, and for time\nsensitive conditions every extra minute has a cost: in acute ischemic stroke, treatment delay is\ndirectly linked to worse outcomes. The problem compounds at\nregional or community hospitals, where a patient needing specialist treatment must first be\nidentified, then transferred to a comprehensive center, a handoff that traditionally depends on a\nradiologist's read, a phone call to a specialist, and a manual transfer process.",[],"1. **Scan and analyze.** As soon as a CT or MRI is acquired, an AI model, cleared for that specific\n   use, analyzes it for the patterns it is trained to detect, such as intracranial hemorrhage, large\n   vessel occlusion or pulmonary embolism.\n2. **Flag and notify.** A positive finding pushes the case to the top of the radiologist's worklist\n   and sends a mobile alert to the on call specialist and care team, often within seconds of the scan\n   completing.\n3. **Confirm and act.** The radiologist reviews the flagged images and confirms or rules out the\n   finding; the specialist team begins the treatment pathway, such as a thrombectomy, transfer or\n   surgery, based on the confirmed read, not the AI flag alone.\n4. **Coordinate transfer.** At a regional hospital without full stroke or trauma capability, the same\n   alert can trigger a standardized transfer protocol and direct communication with a comprehensive\n   center.\n5. **Audit and monitor.** Every flagged and missed case feeds back into ongoing monitoring of\n   sensitivity, specificity and turnaround time by pathology, site and shift.",[34,35,36],"speed","risk-reduction","employee-productivity",[38,39,40],"processing-time-reduction","response-time-reduction","detection-rate-improvement",{"referenceOrg":42,"inputs":43,"formula":71,"currency":72,"period":73,"resultLabel":74,"caveat":75},"A 400 bed hospital reading 40,000 CT and MRI studies a year for time sensitive pathologies",[44,50,57,64],{"key":45,"label":46,"low":47,"high":47,"unit":48,"note":49},"studies","Time sensitive CT and MRI studies read per year",40000,"studies per year","Editorial assumption for a 400 bed hospital's time sensitive imaging volume; replace with your own case mix.",{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"positiveShare","Share of studies with a time sensitive positive finding",0.02,0.05,"fraction of studies","Editorial assumption across intracranial hemorrhage, large vessel occlusion and pulmonary embolism screening; replace with your own case mix.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"minutesSaved","Minutes of care team activation time saved per positive case",10,30,"minutes per case","Below the figure in Viz.ai's release (describing the Adventist Health + Rideout deployment, reporting care team notification time falling from 45 minutes to 7 minutes, a 38 minute reduction, for large vessel occlusion stroke at one hospital), because this input averages across intracranial hemorrhage, large vessel occlusion and pulmonary embolism, and the 38 minute figure covers only large vessel occlusion. This figure is not a recorded metric or a computed benchmark; the evidence record does not report it as a metric because it measures a narrower step, care team notification, than the end to end transfer time the taxonomy's Cycle time reduction KPI defines, and no KPI in this taxonomy covers that step on its own.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"valuePerMinute","Value of a minute of faster time sensitive treatment",50,120,"USD per minute","Editorial assumption combining avoided length of stay, disability and readmission cost for time sensitive conditions; replace with your own health economic estimate.","studies * positiveShare * minutesSaved * valuePerMinute","USD","per year","Annual value of faster time sensitive treatment","A rough proxy for the value of speed only. It leaves out the cost of the software and its integration, the value of pathologies not modeled here, and the fact that faster notification does not guarantee a faster or better clinical outcome for every patient.",[],{"complexity":78,"complexityNote":79,"dataPrerequisites":80,"integrations":84},"high","Analyzing the image is a small part of the work. The device must be cleared or CE marked for the exact indication, scanner types and patient population in use, integrated with PACS and the hospital's paging and care team activation system, and validated on local data, all before it changes a single worklist.",[81,82,83],"A regulatory clearance (FDA clearance or CE mark) that covers the pathology, patient population and scanner protocols in use","PACS and imaging archive integration for the modalities in scope","A defined care team activation and paging workflow to wire the alert into",[85,86,87,88],"Picture archiving and communication system (PACS)","Radiology information system and worklist","Clinician paging and care team activation system","Electronic health record, for the confirmed finding and downstream care pathway",{"steps":90,"guardrails":106,"humanInTheLoop":110,"kpisToInstrument":111,"failureModes":115},[91,94,97,100,103],{"title":92,"detail":93},"Start with one time critical pathology with a clear clinical owner","Pick a pathology such as large vessel occlusion stroke or intracranial hemorrhage where a stroke or trauma lead can own the rollout, rather than deploying every available module at once.",{"title":95,"detail":96},"Confirm the clearance matches your population before go live","Check that the FDA clearance or CE mark covers your scanner models, contrast protocols and patient population; performance on a mismatched population can be unreliable and go unnoticed.",{"title":98,"detail":99},"Wire the alert into the paging system specialists already use","Route the notification through the existing on call and care team activation workflow, not a new inbox nobody checks at 3am.",{"title":101,"detail":102},"Set a turnaround time target per pathology and measure it before and after","Track time from scan completion to notification and to radiologist confirmation, not only sensitivity and specificity.",{"title":104,"detail":105},"Add pathologies and sites one at a time, each with its own validation","Treat every new pathology or site as a new rollout with its own local validation, not an automatic extension of the first one.",[107,108,109],"Every flagged finding is confirmed by a radiologist before it changes a treatment plan; the AI reorders the queue, it does not diagnose","The system only runs within its cleared indications, scanner types and patient population","A defined fallback (acuity based or FIFO ordering) applies when the AI is unavailable or a study fails triage","Radiologists confirm every AI flagged finding before it drives a clinical decision, and review a sample of unflagged cases to catch missed findings. A clinical safety lead owns the pathology's performance against its cleared claims and approves any expansion to a new site or population.",[112,113,114],"Time from scan completion to critical finding notification, by pathology and shift","Radiologist report turnaround time for flagged versus unflagged cases","False positive and false negative rate against a sampled radiologist read",[116,119],{"title":117,"detail":118},"Alert fatigue from false positives","Too many low value alerts and clinicians start deprioritizing all of them, including true positives. Track and act on the false positive rate per pathology and site, not only overall sensitivity.",{"title":120,"detail":121},"Silent underperformance outside the cleared population","The model performs unreliably on a scanner protocol or patient group it was not validated on, and nobody notices because the model gives no signal that it is out of its depth. Validate on local data before go live and monitor for performance drift by site.",{"euAiAct":123,"regulations":125,"guidance":131,"controls":148,"incidents":153},{"tier":78,"basis":124},"Article 6(1) and Annex I: software that analyzes a medical image to detect or prioritize a disease finding is itself, or is a safety component of, a device in scope of the EU Medical Device Regulation, and typically needs a notified body conformity assessment as software as a medical device (the FDA's AI Enabled Medical Device List shows US market authorization for devices in this category, listing authorized stroke triage devices from Viz.ai and Aidoc's BriefCase triage devices), which makes it high risk under the EU AI Act regardless of Annex III. The radiologist's own diagnostic read stays a human decision; the AI narrows and reorders the queue. Annex I high risk classification under Article 6(1) applies from 2 August 2028 (Article 113(c)); until then, Article 4 (AI literacy obligations) and Article 5 (prohibited practices), which bind the hospital as a deployer, already apply.",[126,127,128,129,130],"eu-ai-act","gdpr","hipaa","iso-42001","nist-ai-rmf",[132,138,142],{"title":133,"issuer":134,"region":135,"url":136,"note":137},"Regulation (EU) 2017/745 on medical devices","European Union","europe","https://eur-lex.europa.eu/eli/reg/2017/745/oj","Does not mention AI by name. Software that provides information used to take decisions with diagnostic or therapeutic purposes, such as a finding used to prioritize or route a patient, is classified under Annex VIII Rule 11, usually as class IIa or higher, which requires a notified body conformity assessment before CE marking.",{"title":139,"issuer":134,"region":135,"url":140,"note":141},"Article 6: Classification Rules for High-Risk AI Systems","https://artificialintelligenceact.eu/article/6/","A safety component of, or a product that is itself, a CE marked medical device under EU harmonisation legislation requiring third party conformity assessment is high risk under the EU AI Act. Per Article 113(c), this Annex I route applies from 2 August 2028, later than the 2 December 2027 date for the Annex III use cases.",{"title":143,"issuer":144,"region":145,"url":146,"note":147},"List of Artificial Intelligence-Enabled Medical Devices","US Food and Drug Administration","north-america","https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices","FDA's list of AI enabled medical devices authorized for marketing in the United States. The list names each device and its company, for example \"Viz.ai, Inc.\", along with a decision date and submission number; the cleared or granted indications are in the linked 510(k) or De Novo records. It lists authorized stroke triage devices from Viz.ai and Aidoc's BriefCase triage devices.",[149,150,151,152],"Maintain the regulatory clearance and intended use statement for each detection module, and never enable a pathology it is not cleared for","Radiologist confirms every flagged finding before it changes a treatment plan","Track sensitivity, specificity and turnaround time by pathology, site and shift against the cleared performance claims","Defined fallback ordering when the AI is unavailable or a study fails triage, so the worklist never silently reverts to an unmanaged queue",[],{"howToBuild":155},"Blits.ai does not read medical images; the FDA cleared or CE marked detection model does that, and\nstays outside Blits.ai's scope. What Blits.ai adds is the coordination layer around the alert: an\n**agentic workflow**, triggered through its own API token (or the REST API channel), receives the\nfinding from the imaging system. An **AI agent** turns it into a plain language page to the right\non call specialist, with the patient's context pulled from a **SQL knowledge base**, and a\n**custom function** makes the outbound call to the hospital's paging or care team activation\nsystem. For a regional hospital without full stroke capability, the same workflow, with **human in\nthe loop approval** before any transfer request goes out, encodes the standardized transfer\nprotocol to a comprehensive center.\n\n**Monitors** run scheduled health checks against the agent that writes the specialist facing\npage, with email or webhook alerts when it fails, and **analytics** on the workflow's run\nhistory track time from finding to notification. **Guardrails** apply LLM based content\nchecks to keep the specialist facing page in plain, unambiguous language; they reduce the risk of\na garbled or misleading message but cannot themselves guarantee that only a confirmed finding is\never relayed. That guarantee comes from the **human in the loop approval** step, not from\nguardrails, which is why no transfer request goes out before a radiologist has acted.",[157,160,163,166],{"question":158,"answer":159},"What does AI radiology triage actually do?","It analyzes an image right after the scan, flags time sensitive findings it is cleared to detect, and reorders the radiologist's worklist and alerts the care team so urgent cases are read first. It does not replace the radiologist's diagnostic read.",{"question":161,"answer":162},"Does the AI diagnose the patient?","No. The AI flags a likely finding and reprioritizes the queue; a radiologist confirms or rules out the finding before any treatment decision is made.",{"question":164,"answer":165},"Is AI radiology triage regulated as a medical device?","Generally yes. In the United States these tools typically hold FDA clearance as software as a medical device, and in the EU they are usually CE marked medical devices under Annex VIII Rule 11 of the Medical Device Regulation. Because that classification requires a notified body conformity assessment, they are high risk under the EU AI Act's Annex I route, regardless of whether the specific use appears in Annex III, though that Annex I classification only takes effect on 2 August 2028.",{"question":167,"answer":168},"How much time does it actually save?","It depends heavily on the pathology, the baseline workflow and the hospital. According to Viz.ai, a study led by Adventist Health + Rideout's stroke program manager and presented at the 2026 International Stroke Conference found that average door in door out transfer time for large vessel occlusion stroke patients fell by 44%, from 202 to 113 minutes, after a quality improvement program that included the Viz.ai platform, a partnership with a comprehensive stroke center and standardized transfer protocols.",[],"2026-09-28",[172],{"date":170,"note":173},"First published","radiology-worklist-triage",[176,205],{"title":177,"useCases":178,"organization":179,"vendors":184,"summary":188,"stage":189,"year":190,"channels":191,"languages":192,"metrics":193,"outcomeDisclosed":194,"sources":195,"verification":200,"grade":202,"id":203,"organizationSlug":204},"Sheba Medical Center: AI triage for intracranial hemorrhage",[174],{"name":180,"anonymized":181,"country":182,"region":183,"industry":17},"Sheba Medical Center",false,"IL","middle-east",[185],{"name":186,"role":187},"Aidoc","platform","Sheba Medical Center, where Aidoc originated, has embedded Aidoc's AI platform across its emergency and radiology workflows to flag urgent findings, including intracerebral hemorrhage, large vessel occlusion stroke and pulmonary embolism, directly on images in real time, and alert the treating physician on desktop and mobile. Sheba's own account of the deployment cites a peer reviewed clinical study that found the integration of Aidoc into its emergency workflow was associated with a 30% reduction in mortality for patients with intracerebral hemorrhage, earlier treatment initiation, improved discharge outcomes and fewer unnecessary ICU stays. Sheba's own site frames Aidoc as one part of its wider Smart Hospital program, and lists a separate initiative, Project K, an AI powered emergency room, as a related case study; the page does not describe Project K as an extension of Aidoc's triage.","scaled",2025,[25],[],[],true,[196],{"url":197,"title":198,"publisher":180,"archivedUrl":199},"https://sheba-global.com/project/aidoc/","Aidoc: Real-Time AI-Powered Radiology at Sheba","https://web.archive.org/web/20251225075441/https://sheba-global.com/project/aidoc/",{"level":201,"checkedAt":170},"source-verified","B","sheba-medical-center-aidoc-triage",null,{"title":206,"useCases":207,"organization":208,"vendors":211,"summary":214,"stage":215,"year":216,"channels":217,"languages":218,"metrics":220,"outcomeDisclosed":194,"sources":230,"verification":234,"grade":235,"id":236,"organizationSlug":204},"Adventist Health + Rideout: AI stroke transfer time reduction",[174],{"name":209,"anonymized":181,"country":210,"region":145,"industry":17},"Adventist Health + Rideout","US",[212],{"name":213,"role":187},"Viz.ai","Adventist Health + Rideout, a regional primary stroke center in a hub and spoke network, used the Viz.ai platform's real time imaging analysis and automated care coordination as part of a quality improvement initiative to speed up the transfer of large vessel occlusion stroke patients to a comprehensive stroke center. The program combined the AI platform with a partnership with a comprehensive stroke center and standardized transfer protocols. Data presented at the American Heart Association's 2026 International Stroke Conference, led by the hospital's stroke program manager Caezar G. Jara, showed the changes cut average door in door out transfer time to 113 minutes, below the Joint Commission's 120 minute national benchmark.","production",2026,[25],[219],"en",[221],{"kpi":38,"value":222,"unit":223,"qualifier":224,"period":225,"baseline":226,"claimant":227,"quote":228,"sourceUrl":229},44,"percent","approximately","quality improvement initiative combining Viz.ai platform deployment, a partnership with a comprehensive stroke center, and standardized transfer protocols, presented at ISC 2026","202 minutes average door in door out (DIDO) time before the quality improvement initiative","vendor","Viz.ai, the leader in AI-powered disease detection and intelligent care coordination, today announced the presentation of new clinical data at the American Heart Association's International Stroke Conference (ISC) 2026 demonstrating a 44% reduction in door-in-door-out (DIDO) time — the time required to evaluate, coordinate, and transfer a patient to a comprehensive stroke center — for patients with large vessel occlusion (LVO) stroke in regional care settings.","https://www.viz.ai/news/viz-ai-study-demonstrates-44-reduction-in-interfacility-stroke-transfer-times",[231],{"url":229,"title":232,"publisher":213,"date":233},"Viz.ai Study Demonstrates 44% Reduction in Interfacility Stroke Transfer Times","2026-03-05",{"level":201,"checkedAt":170},"C","adventist-health-rideout-viz-ai-stroke-transfer",0,[239],{"kpi":38,"label":240,"unit":223,"aggregate":194,"higherIsBetter":194,"n":241,"nUpTo":237,"median":222,"min":222,"max":222,"byClaimant":242,"vendorOnly":194,"points":243},"Cycle time reduction",1,{"organization":237,"vendor":241,"regulator":237,"independent":237},[244],{"evidenceId":236,"organization":209,"value":222,"qualifier":224,"claimant":227,"grade":235,"pooled":194},{"low":246,"high":247},400000,7200000,[249,270,301,320],{"slug":250,"title":251,"shortTitle":252,"definition":253,"status":9,"industries":254,"functions":255,"patterns":257,"audience":26,"autonomy":27,"adoptionStage":259,"segment":260,"evidenceCount":261,"publicEvidenceCount":261,"organizations":262,"bestGrade":202,"headline":265,"lastVerified":170,"indexable":194},"hospital-bed-and-staff-capacity-command-center","AI command center for hospital bed and staff capacity planning","Hospital capacity command center","An AI powered operations center that predicts patient admissions, discharges and transfers across a hospital or health system, and helps a team of coordinators sitting in one room sequence real time bed assignments, staffing levels and patient moves, so patients get into the right bed faster and existing capacity is used fully without adding beds.",[17],[19,256],"analytics-and-reporting",[258,22,23],"prediction-and-scoring","early-adopters","hospital operations",2,[263,264],"Humber River Health","Johns Hopkins Medicine",{"kpi":38,"label":240,"unit":223,"n":261,"nUpTo":237,"kind":266,"value":267,"qualifier":268,"claimant":269,"organization":264,"vendorReported":181},"reported",38,"exact","organization",{"slug":271,"title":272,"shortTitle":273,"definition":274,"status":9,"industries":275,"functions":280,"patterns":282,"audience":26,"autonomy":286,"adoptionStage":28,"evidenceCount":287,"publicEvidenceCount":288,"organizations":289,"bestGrade":202,"headline":296,"lastVerified":300,"indexable":194},"it-service-desk-resolution-agent","AI agent for IT service desk resolution","IT service desk resolution","An AI agent in Microsoft Teams, Slack or the intranet that takes the high volume IT support queue, such as password and MFA resets, account unlocks, VPN, device and software requests, and resolves common requests by acting in the identity and IT service management systems, handing the rest to the right resolver group with the context attached.",[276,277,278,279,17],"cross-industry","banking","technology","retail-and-ecommerce",[281,19],"it-and-engineering",[283,284,285,22],"conversational-agent","agentic-workflow","rag-knowledge-assistant","supervised-agent",8,6,[290,291,292,293,294,295],"7-Eleven Vietnam","Bank of America","Equinix","IBM","Mercari US","Vituity",{"kpi":297,"label":298,"unit":223,"n":261,"nUpTo":237,"kind":266,"value":299,"qualifier":268,"claimant":227,"organization":294,"vendorReported":194},"employee-adoption","Employee adoption",94,"2026-09-27",{"slug":302,"title":303,"shortTitle":304,"definition":305,"status":9,"industries":306,"functions":308,"patterns":309,"audience":26,"autonomy":27,"adoptionStage":259,"evidenceCount":311,"publicEvidenceCount":311,"organizations":312,"bestGrade":202,"headline":316,"lastVerified":300,"indexable":194},"clinical-trial-patient-matching","AI clinical trial patient matching and prescreening","Clinical trial patient matching","AI that reads structured data and clinical notes in the health record, compares each patient with the inclusion and exclusion criteria of open clinical trials, and gives research staff and treating clinicians a ranked list of likely eligible patients with the evidence for each criterion, so that people confirm eligibility and invite the patient.",[17,307],"pharma-and-life-sciences",[19,256],[310,22],"document-processing",3,[313,314,315],"Cleveland Clinic","Mount Sinai Health System","Yale Cancer Center",{"kpi":317,"label":318,"unit":223,"n":241,"nUpTo":237,"kind":266,"value":319,"qualifier":268,"claimant":269,"organization":313,"vendorReported":181},"accuracy","Accuracy",100,{"slug":321,"title":322,"shortTitle":323,"definition":324,"status":9,"industries":325,"functions":327,"patterns":329,"audience":26,"autonomy":331,"adoptionStage":259,"segment":332,"evidenceCount":288,"publicEvidenceCount":288,"organizations":333,"bestGrade":202,"headline":340,"lastVerified":300,"indexable":194},"network-fault-triage-copilot","AI copilot for network operations centre fault triage","NOC fault triage copilot","AI in the network operations centre (NOC) that correlates alarms and performance data from radio, transport, core and fixed networks into a small number of probable faults, ranks them by customer impact, proposes the likely root cause and fix from runbooks, vendor documentation and past tickets, and routes the ticket to the right team, while an engineer decides what to change.",[326],"telecommunications",[328,19],"network-operations",[23,22,285,330,284],"summarization","copilot","network",[334,335,336,337,338,339],"Bell Canada","Deutsche Telekom","KDDI","Orange","Telstra","Vodafone",{"kpi":38,"label":240,"unit":223,"n":241,"nUpTo":237,"kind":266,"value":341,"qualifier":342,"claimant":269,"organization":335,"vendorReported":181},95,"at-least",{"indexable":194,"reasons":344},[],[346,351,356,363,369,375,382,389,397,404,411,417,424,431,437,442,449,455,460,466,472,478,483,488,493,500,506,511,516,523,530,536,542,547],{"id":126,"label":347,"issuer":134,"region":135,"url":348,"description":349,"useCases":350,"indexable":194},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":127,"label":352,"issuer":134,"region":135,"url":353,"description":354,"useCases":355,"indexable":194},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":129,"label":357,"issuer":358,"region":359,"url":360,"description":361,"useCases":362,"indexable":194},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":130,"label":364,"issuer":365,"region":145,"url":366,"description":367,"useCases":368,"indexable":194},"NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":370,"label":371,"issuer":134,"region":135,"url":372,"description":373,"useCases":374,"indexable":194},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":376,"label":377,"issuer":378,"region":135,"url":379,"description":380,"useCases":381,"indexable":194},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":383,"label":384,"issuer":385,"region":135,"url":386,"description":387,"useCases":388,"indexable":194},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":390,"label":391,"issuer":392,"region":393,"url":394,"description":395,"useCases":396,"indexable":194},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":398,"label":399,"issuer":400,"region":393,"url":401,"description":402,"useCases":403,"indexable":194},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":405,"label":406,"issuer":407,"region":359,"url":408,"description":409,"useCases":410,"indexable":194},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":412,"label":413,"issuer":414,"region":145,"url":415,"description":416,"useCases":410,"indexable":194},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":418,"label":419,"issuer":420,"region":135,"url":421,"description":422,"useCases":423,"indexable":194},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":425,"label":426,"issuer":427,"region":359,"url":428,"description":429,"useCases":430,"indexable":194},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":432,"label":433,"issuer":134,"region":135,"url":434,"description":435,"useCases":436,"indexable":194},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":438,"label":439,"issuer":134,"region":135,"url":440,"description":441,"useCases":436,"indexable":194},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":443,"label":444,"issuer":445,"region":145,"url":446,"description":447,"useCases":448,"indexable":194},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":450,"label":451,"issuer":134,"region":135,"url":452,"description":453,"useCases":454,"indexable":194},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":128,"label":456,"issuer":457,"region":145,"url":458,"description":459,"useCases":454,"indexable":194},"HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":461,"label":462,"issuer":463,"region":359,"url":464,"description":465,"useCases":454,"indexable":194},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":467,"label":468,"issuer":134,"region":135,"url":469,"description":470,"useCases":471,"indexable":194},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":473,"label":474,"issuer":475,"region":145,"url":476,"description":477,"useCases":471,"indexable":194},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-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":479,"label":480,"issuer":392,"region":393,"url":481,"description":482,"useCases":60,"indexable":194},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":484,"label":485,"issuer":134,"region":135,"url":486,"description":487,"useCases":60,"indexable":194},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":489,"label":490,"issuer":134,"region":135,"url":491,"description":492,"useCases":60,"indexable":194},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":494,"label":495,"issuer":496,"region":135,"url":497,"description":498,"useCases":499,"indexable":194},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":501,"label":502,"issuer":503,"region":145,"url":504,"description":505,"useCases":287,"indexable":194},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":507,"label":508,"issuer":134,"region":135,"url":509,"description":510,"useCases":287,"indexable":194},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":512,"label":513,"issuer":134,"region":135,"url":514,"description":515,"useCases":288,"indexable":194},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":517,"label":518,"issuer":519,"region":183,"url":520,"description":521,"useCases":522,"indexable":194},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":524,"label":525,"issuer":526,"region":135,"url":527,"description":528,"useCases":529,"indexable":194},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",4,{"id":531,"label":532,"issuer":533,"region":135,"url":534,"description":535,"useCases":529,"indexable":194},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":537,"label":538,"issuer":539,"region":393,"url":540,"description":541,"useCases":311,"indexable":194},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":543,"label":544,"issuer":134,"region":135,"url":545,"description":546,"useCases":311,"indexable":194},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":548,"label":549,"issuer":550,"region":145,"url":551,"description":552,"useCases":311,"indexable":194},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598303201]