[{"data":1,"prerenderedAt":608},["ShallowReactive",2],{"uc-breast-cancer-screening-mammography-reading":3,"uc-regulations":381},{"useCase":4,"evidence":159,"blitsAiDeployments":257,"benchmarks":258,"indicative":272,"related":275,"indexability":379,"includeUnpublished":165},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":20,"channels":23,"audience":25,"autonomy":26,"adoptionStage":27,"problem":28,"problemStats":29,"howItWorks":30,"valueDrivers":31,"kpis":35,"indicativeValue":40,"macroEstimates":73,"feasibility":74,"implementation":85,"risk":117,"blitsAi":140,"faq":142,"related":152,"datePublished":154,"dateModified":154,"lastVerified":154,"changelog":155,"slug":158},"AI support for reading screening mammograms in breast cancer screening programs","Screening mammography reading","AI for breast cancer screening mammography","AI that scores screening mammograms and supports or triages radiologist reading. A Danish region reported a 33.5% lower reading workload and a Swedish trial 44.2%.","published","AI support for screening mammography scores every examination in a population based breast cancer screening program and either triages it to single or double reading or acts as decision support for the radiologists who read it. A radiologist still makes every recall decision.",[12,13,14,15],"AI mammography screening","AI supported double reading","AI second reader for mammograms","AI breast screening triage",[17],"healthcare",[19],"operations",[21,22],"computer-vision","prediction-and-scoring",[24],"internal-tools","employee-facing","copilot","early-adopters","Organized breast cancer screening invites large numbers of healthy women every two or three\nyears, and many programs, especially in Europe, have two radiologists read every examination. Almost every examination is\nnormal, so most of the reading effort goes into confirming that nothing is there, while the few\ncancers have to be found among them. Programs that must keep double reading as a safeguard run\nshort of breast radiologists before they run short of women to screen.\n\nAI changes how that reading time is spent. The system gives each examination a risk score and marks\nsuspicious areas. Programs use the score in two ways: to send examinations that are likely\nnormal to one reader instead of two, and to show the marked areas to the radiologists who read the\nrest. The evidence on this page comes from programs that kept a radiologist in every reading and\ncompared cancer detection, recall and workload with standard double reading: a randomized trial in\nSweden, a before and after evaluation in Denmark and an observational study at 12 German sites.",[],"1. **Score each examination.** After the mammogram is taken, an image analysis model assigns a\n   risk score and marks suspicious lesions and calcifications.\n2. **Route by score.** Examinations below a set threshold are read by one senior breast\n   radiologist. Examinations above it go to two radiologists, who see the AI markings as decision\n   support. Some programs keep double reading for all examinations and use the AI only as a\n   second look.\n3. **Read and decide.** The radiologists decide whether to recall the woman for assessment. The\n   AI never recalls or discharges anyone on its own.\n4. **Monitor the program.** Cancer detection, recall, false positives, interval cancers and the\n   reading workload are tracked against the period before AI and checked on a regular schedule.",[32,33,34],"employee-productivity","risk-reduction","speed",[36,37,38,39],"productivity-gain","detection-rate-improvement","false-positive-reduction","accuracy",{"referenceOrg":41,"inputs":42,"formula":68,"currency":69,"period":70,"resultLabel":71,"caveat":72},"A regional breast screening program with 60,000 screening examinations a year",[43,48,54,61],{"key":44,"label":45,"low":46,"high":46,"unit":44,"note":47},"examinations","Screening examinations per year",60000,"The reference program.",{"key":49,"label":50,"low":51,"high":51,"unit":52,"note":53},"readsPerExam","Radiologist reads per examination before AI",2,"reads per examination","Standard double reading, as in the Danish and Swedish programs on this page.",{"key":55,"label":56,"low":57,"high":58,"unit":59,"note":60},"workloadReduction","Share of reads saved",0.2,0.33,"fraction of reads","Conservative against the benchmarks on this page (the Capital Region of Denmark reports a 33.5% reduction in reading workload and the Swedish MASAI trial 44.2%), because a first launch often keeps more double reading.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"costPerRead","Cost of one radiologist read",5,15,"USD per read","Editorial assumption, replace with your own fully loaded radiologist cost per examination read.","examinations * readsPerExam * workloadReduction * costPerRead","USD","per year","Radiologist reading cost released","Gross reading cost only. It leaves out the license and integration cost of the AI, the extra assessment work for any additional cancers found, and the benefit of earlier detection.",[],{"complexity":75,"complexityNote":76,"dataPrerequisites":77,"integrations":81},"high","The image analysis model is a certified medical device bought from a vendor, so the build is the connection to the PACS and the screening information system, the reading protocol with its thresholds, a clinical validation on the program's own population and ongoing audit of cancers and interval cancers.",[78,79,80],"Screening examinations in DICOM format with the program's own outcome data","A written reading protocol that says which examinations get one reader and which get two","Follow up data on biopsies, surgery and interval cancers to audit the thresholds",[82,83,84],"PACS and the screening program information system","Radiologist reading workstation, so the markings show during the read","Recall and invitation system of the screening program",{"steps":86,"guardrails":99,"humanInTheLoop":104,"kpisToInstrument":105,"failureModes":110},[87,90,93,96],{"title":88,"detail":89},"Validate on your own population","Run the AI silently on past examinations with known outcomes and compare its scores with the radiologists' reads and the later cancers, before it influences any reading.",{"title":91,"detail":92},"Write the reading protocol","Decide the score thresholds, who reads which examinations and how a disagreement between the AI and a radiologist is settled. Put the protocol under clinical governance.",{"title":94,"detail":95},"Start with decision support","Show the markings to the radiologists first while every examination is still double read, then move likely normal examinations to single reading once the numbers hold.",{"title":97,"detail":98},"Audit on a schedule","Review cancer detection, recall, false positives and interval cancers every quarter and adjust the threshold when the numbers move.",[100,101,102,103],"A radiologist makes every recall decision, never the AI","A written protocol with fixed thresholds that only the clinical lead can change","Interval cancer review so missed cancers in the single read group are found","Separate monitoring of results by age group and screening round","Breast radiologists read every examination, alone or in pairs, and own the recall decision. The clinical lead owns the threshold and reviews the audit of detected and missed cancers.",[106,107,108,109],"Cancer detection rate per 1,000 screens, compared with the period before AI","Recall rate and false positive rate","Interval cancer rate and stage at diagnosis","Screen reading workload in reads per examination",[111,114],{"title":112,"detail":113},"Radiologists follow the score too closely","Readers accept a low score and stop looking. Prevent with training, blinded sampling and a review of cancers missed in the single read group.",{"title":115,"detail":116},"Threshold drift","A threshold set on one population or scanner performs differently on another. Recheck on the program's own data after every change to the equipment or the invited group.",{"euAiAct":118,"regulations":120,"guidance":123,"controls":134,"incidents":139},{"tier":75,"basis":119},"Article 6(1) with Annex I: an AI system that is a medical device under the Medical Device Regulation (EU) 2017\u002F745, or a safety component of one, is high risk when the device needs a third party conformity assessment. Software that analyzes mammograms to support diagnosis is normally class IIa or higher, which needs a notified body, so it carries the AI Act duties for risk management, data governance, logging and human oversight on top of the medical device route. It is not an Annex III use.",[121,122],"eu-ai-act","gdpr",[124,130],{"title":125,"issuer":126,"region":127,"url":128,"note":129},"Article 6, classification rules for high risk AI systems","European Union","europe","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","An AI system that is a medical device, or a safety component of one, is high risk when a third party conformity assessment is required.",{"title":131,"issuer":126,"region":127,"url":132,"note":133},"Regulation (EU) 2017\u002F745 on medical devices","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2017\u002F745\u002Foj","Software that provides information used to take decisions with diagnostic purposes is classified under Annex VIII Rule 11, usually as class IIa or higher, which requires a notified body conformity assessment before CE marking.",[135,136,137,138],"Documented clinical validation of the model on the program's population before use","Reading protocol under clinical governance with logged threshold changes","Audit trail that shows the score, the readers and the recall decision per examination","Regular review of detected and interval cancers",[],{"howToBuild":141},"The image analysis is done by a certified mammography AI product, and Blits.ai does not read\nimages or score examinations. Blits.ai runs the conversations around the program. An **agent** answers\nstaff questions about the reading protocol, thresholds and audit procedure from a **knowledge base** of the\nprogram's own documents, and a **SQL knowledge base** connected to the program's audit tables lets the\nclinical lead ask for recall and reading workload figures in plain language.\n\nFor invited women, an agent on **web chat, WhatsApp or voice** answers questions about the invitation, the\nappointment and what a recall means, with **flows** and **custom functions** that rebook through the program's booking system, and **human handover** to screening staff for\nanything clinical. **Test suites** check that answers stay the same after every change, and **guardrails** and\n**PII masking** keep patient identifiers out of prompts. Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises.",[143,146,149],{"question":144,"answer":145},"Does AI replace the second radiologist in breast screening?","In the programs on this page it does not replace a radiologist. In the Capital Region of Denmark, likely normal examinations go to one senior breast radiologist and the others are read by two with AI support, so a person makes every recall decision. The region reports that this reduced the reading workload by 33.5%.",{"question":147,"answer":148},"Does AI find more cancers than standard double reading?","The German PRAIM study reports a breast cancer detection rate of 6.7 per 1,000 with AI supported double reading against 5.7 per 1,000 with standard double reading, with a recall rate that was not worse. The study is observational, so programs should audit interval cancers on their own data.",{"question":150,"answer":151},"Is screening mammography AI a high risk system under the EU AI Act?","Usually yes, because mammography software is a medical device and falls under the high risk rules for products that need a third party conformity assessment. The vendor carries the conformity work, and the screening program carries the duties of a deployer.",[153],"radiology-worklist-triage","2026-11-12",[156],{"date":154,"note":157},"First published","breast-cancer-screening-mammography-reading",[160,200,225],{"title":161,"useCases":162,"organization":163,"vendors":168,"summary":169,"stage":170,"year":171,"channels":172,"languages":173,"metrics":175,"outcomeDisclosed":185,"sources":186,"verification":195,"grade":197,"id":198,"organizationSlug":199},"Capital Region of Denmark: AI in population based breast cancer screening",[158],{"name":164,"anonymized":165,"country":166,"region":127,"industry":17,"wikidata":167},"Capital Region of Denmark",false,"DK","Q26073",[],"The Capital Region of Denmark brought an AI system into its biennial mammography screening of women aged 50 to 69 on 2021-11-18. The system scores every screening examination. Examinations the AI rates as likely normal are read once by a senior breast radiologist, and the rest are read by two radiologists with AI decision support. An evaluation by the University of Copenhagen and the region's screening program, published in Radiology, compares 60,751 women screened before AI with 58,246 screened after. Recall fell, cancer detection rose and the reading workload dropped by a third.","scaled",2021,[24],[174],"da",[176],{"kpi":36,"value":177,"unit":178,"qualifier":179,"period":180,"baseline":181,"claimant":182,"quote":183,"sourceUrl":184},33.5,"percent","exact","Saved screening reads after AI implementation, 2021-11-18 to 2022-10-17, versus the double reading period before","Double reading of every screening before AI implementation","organization","The reading workload was reduced by 33.5% (38 977 of 116 492 reads).","https:\u002F\u002Fwww.ebi.ac.uk\u002Feuropepmc\u002Fwebservices\u002Frest\u002Fsearch?query=EXT_ID:38832880&resultType=core&format=json",true,[187,191],{"url":184,"title":188,"publisher":189,"date":190},"Early Indicators of the Impact of Using AI in Mammography Screening for Breast Cancer (abstract via Europe PMC)","Radiology","2024-06-01",{"url":192,"title":193,"publisher":194},"https:\u002F\u002Fdi.ku.dk\u002Fenglish\u002Fnews\u002F2024\u002Fai-successfully-helps-detecting-more-breast-cancers-around-danish-capital","AI successfully helps detecting more breast cancers around Danish capital","University of Copenhagen, Department of Computer Science",{"level":196,"checkedAt":154},"source-verified","B","capital-region-denmark-ai-mammography-screening",null,{"title":201,"useCases":202,"organization":203,"vendors":206,"summary":207,"stage":170,"year":171,"channels":208,"languages":209,"metrics":211,"outcomeDisclosed":185,"sources":218,"verification":223,"grade":197,"id":224,"organizationSlug":199},"German mammography screening program: AI supported double reading (PRAIM study)",[158],{"name":204,"anonymized":165,"country":205,"region":127,"industry":17},"German mammography screening program (PRAIM study sites)","DE",[],"In the PRAIM implementation study, 119 radiologists at 12 sites of the German organized mammography screening program screened 463,094 women aged 50 to 69 between July 2021 and February 2023, 260,739 of them with AI support. Radiologists chose voluntarily whether to use the AI system in a double reading. The AI supported group had a higher breast cancer detection rate and a recall rate that was not worse than standard double reading. The study is observational, not randomized, and was published in Nature Medicine in 2025.",[24],[210],"de",[212],{"kpi":37,"value":213,"unit":178,"qualifier":179,"period":214,"baseline":215,"claimant":182,"quote":216,"sourceUrl":217},17.6,"Breast cancer detection rate, AI supported double reading versus standard double reading, July 2021 to February 2023","Standard double reading without AI (5.7 per 1,000)","Radiologists in the AI-supported screening group achieved a breast cancer detection rate of 6.7 per 1,000, which was 17.6% (95% confidence interval: +5.7%, +30.8%) higher than and statistically superior to the rate (5.7 per 1,000) achieved in the control group.","https:\u002F\u002Fwww.ebi.ac.uk\u002Feuropepmc\u002Fwebservices\u002Frest\u002Fsearch?query=EXT_ID:39775040&resultType=core&format=json",[219],{"url":217,"title":220,"publisher":221,"date":222},"Nationwide real-world implementation of AI for cancer detection in population-based mammography screening (abstract via Europe PMC)","Nature Medicine","2025-01-07",{"level":196,"checkedAt":154},"german-mammography-screening-praim-ai-supported-double-reading",{"title":226,"useCases":227,"organization":228,"vendors":231,"summary":235,"stage":236,"year":171,"channels":237,"languages":238,"metrics":240,"outcomeDisclosed":185,"sources":247,"verification":255,"grade":197,"id":256,"organizationSlug":199},"Southwest Sweden mammography screening: AI supported reading in the MASAI trial",[153,158],{"name":229,"anonymized":165,"country":230,"region":127,"industry":17},"Mammography screening programme in southwest Sweden (Malmö, Lund, Landskrona, Trelleborg)","SE",[232],{"name":233,"role":234},"ScreenPoint Medical","platform","In the Mammography Screening with Artificial Intelligence trial (MASAI), women eligible for the Swedish national screening programme at four sites in southwest Sweden (Malmö, Lund, Landskrona and Trelleborg) were randomly assigned to AI supported screening or standard double reading. The AI system, Transpara version 1.7.0 from ScreenPoint Medical, triaged each examination to single or double reading and highlighted suspicious findings. Between April 2021 and December 2022, 105,934 women were randomized. AI supported screening detected more cancers without a significant rise in recalls or false positives and cut the screen reading workload. The trial, run by Lund University researchers, is published in The Lancet Digital Health.","pilot",[24],[239],"sv",[241],{"kpi":36,"value":242,"unit":178,"qualifier":179,"period":243,"baseline":244,"claimant":182,"quote":245,"sourceUrl":246},44.2,"Screen readings in the intervention group versus the control group, April 12, 2021 to December 7, 2022","Standard double reading (control group)","There were 61 248 screen readings in the intervention group and 109 692 in the control group, resulting in a 44·2% reduction in the screen-reading workload.","https:\u002F\u002Fwww.ebi.ac.uk\u002Feuropepmc\u002Fwebservices\u002Frest\u002Fsearch?query=EXT_ID:39904652&resultType=core&format=json",[248,252],{"url":246,"title":249,"publisher":250,"date":251},"Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI) (abstract via Europe PMC)","The Lancet Digital Health","2025-02-03",{"url":253,"title":254,"publisher":250,"date":251},"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs2589-7500(24)00267-x","Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI)",{"level":196,"checkedAt":154},"southwest-sweden-masai-ai-supported-mammography-screening",0,[259,266],{"kpi":36,"label":260,"unit":178,"aggregate":185,"higherIsBetter":185,"n":51,"nUpTo":257,"median":261,"min":177,"max":242,"byClaimant":262,"vendorOnly":165,"points":263},"Productivity gain",38.85,{"organization":51,"vendor":257,"regulator":257,"independent":257},[264,265],{"evidenceId":256,"organization":229,"value":242,"qualifier":179,"claimant":182,"grade":197,"pooled":185},{"evidenceId":198,"organization":164,"value":177,"qualifier":179,"claimant":182,"grade":197,"pooled":185},{"kpi":37,"label":267,"unit":178,"aggregate":185,"higherIsBetter":185,"n":268,"nUpTo":257,"median":213,"min":213,"max":213,"byClaimant":269,"vendorOnly":165,"points":270},"Detection improvement",1,{"organization":268,"vendor":257,"regulator":257,"independent":257},[271],{"evidenceId":224,"organization":204,"value":213,"qualifier":179,"claimant":182,"grade":197,"pooled":185},{"low":273,"high":274},120000,594000,[276,305,327,354],{"slug":153,"title":277,"shortTitle":278,"definition":279,"status":9,"industries":280,"functions":281,"patterns":282,"audience":25,"autonomy":285,"adoptionStage":286,"segment":287,"evidenceCount":288,"publicEvidenceCount":288,"organizations":289,"bestGrade":197,"headline":302,"lastVerified":304,"indexable":185},"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],[21,283,284],"classification-and-routing","anomaly-detection","assist","mainstream","emergency and inpatient imaging",13,[290,291,292,293,294,295,296,297,298,229,299,300,301],"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":36,"label":260,"unit":178,"n":51,"nUpTo":257,"kind":303,"value":242,"qualifier":179,"claimant":182,"organization":229,"vendorReported":165},"reported","2026-09-29",{"slug":306,"title":307,"shortTitle":308,"definition":309,"status":9,"industries":310,"functions":313,"patterns":315,"audience":319,"autonomy":320,"adoptionStage":27,"evidenceCount":321,"publicEvidenceCount":322,"organizations":323,"bestGrade":197,"headline":199,"lastVerified":326,"indexable":185},"outbound-reminder-and-confirmation-agent","AI agent for outbound reminders and confirmations by voice and messaging","Outbound reminders and confirmations","An AI agent for outbound reminders and confirmations contacts customers about something they already booked or ordered, to remind them and confirm attendance, for bookings outside clinical care, such as deliveries, reservations, service visits and public service appointments. The agent lets them cancel or move the booking in the same conversation, by phone, SMS, WhatsApp or email. This is operational service outreach, not marketing: nothing is sold, and success is measured in kept appointments and reused slots, not in conversion. Patient appointment reminders, which must respect clinical rules and the health record, are covered in depth by the patient scheduling and reminders agent.",[311,17,312],"cross-industry","government",[314,19],"customer-service",[316,317,318,22],"voice-agent","conversational-agent","agentic-workflow","customer-facing","supervised-agent",4,3,[324,325,301],"Sheffield Children's NHS Foundation Trust","U.S. Department of Veterans Affairs","2026-09-26",{"slug":328,"title":329,"shortTitle":330,"definition":331,"status":9,"industries":332,"functions":333,"patterns":334,"audience":319,"autonomy":320,"adoptionStage":27,"evidenceCount":335,"publicEvidenceCount":288,"organizations":336,"bestGrade":197,"headline":348,"lastVerified":353,"indexable":185},"patient-appointment-scheduling-and-reminders-agent","AI agent for patient appointment scheduling, reminders and no show reduction","Patient scheduling and reminders","An AI agent for patient scheduling and reminders books, moves and cancels patient appointments by phone and messaging, and reminds and confirms patients. It follows the provider's scheduling rules (referral, triage level, clinician and visit type, preparation), confirms and reminds patients in two way conversations, predicts who is likely to miss an appointment, and offers freed slots to patients on the waiting list. Unlike a general branch and appointment booking agent, it writes into the electronic health record and must respect clinical constraints, so anything clinical goes to staff.",[17],[314,19],[316,317,22,318],16,[337,338,339,340,341,342,343,344,324,345,346,301,347],"Audibel","Emirates Health Services","Erasmus MC","Good Shepherd Rehabilitation Network","Howard Brown Health","Mid and South Essex NHS Foundation Trust","North Kansas City Hospital","Reid Health","University Hospitals Coventry and Warwickshire NHS Trust","UMC Utrecht","Zuyderland Medisch Centrum",{"kpi":349,"label":350,"unit":178,"n":64,"nUpTo":257,"kind":351,"value":352,"qualifier":179,"claimant":199,"organization":199,"vendorReported":165},"no-show-reduction","No show reduction","median",30,"2026-09-27",{"slug":355,"title":356,"shortTitle":357,"definition":358,"status":9,"industries":359,"functions":360,"patterns":362,"audience":25,"autonomy":285,"adoptionStage":27,"segment":363,"evidenceCount":364,"publicEvidenceCount":364,"organizations":365,"bestGrade":197,"headline":374,"lastVerified":378,"indexable":185},"hospital-bed-and-staff-capacity-command-center","AI command center for hospital bed and staff capacity planning","Hospital capacity command center","An AI command center for hospital bed and staff capacity planning predicts patient admissions, discharges and transfers, and helps a team of coordinators sequence bed assignments, staffing and moves. The coordinators sit in one room and act on real time predictions across a hospital or health system, so patients get into the right bed faster and existing capacity is used fully without adding beds.",[17],[19,361],"analytics-and-reporting",[22,283,284],"hospital operations",8,[366,367,368,369,370,371,372,373],"Children's Mercy Kansas City","Duke Health","Humber River Health","Johns Hopkins Medicine","King Faisal Specialist Hospital and Research Centre (KFSHRC)","Providence Swedish","The Queen's Health Systems","Tampa General Hospital",{"kpi":375,"label":376,"unit":178,"n":364,"nUpTo":257,"kind":351,"value":377,"qualifier":179,"claimant":199,"organization":199,"vendorReported":165},"processing-time-reduction","Cycle time reduction",36,"2026-09-28",{"indexable":185,"reasons":380},[],[382,386,391,399,407,414,420,427,434,441,447,453,460,466,473,480,486,492,497,503,508,513,520,525,531,538,545,550,555,562,567,573,579,586,591,597,602],{"id":121,"label":383,"issuer":126,"region":127,"url":128,"description":384,"useCases":385,"indexable":185},"EU AI Act","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.",306,{"id":122,"label":387,"issuer":126,"region":127,"url":388,"description":389,"useCases":390,"indexable":185},"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.",276,{"id":392,"label":393,"issuer":394,"region":395,"url":396,"description":397,"useCases":398,"indexable":185},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",149,{"id":400,"label":401,"issuer":402,"region":403,"url":404,"description":405,"useCases":406,"indexable":185},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","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.",114,{"id":408,"label":409,"issuer":410,"region":127,"url":411,"description":412,"useCases":413,"indexable":185},"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.",87,{"id":415,"label":416,"issuer":126,"region":127,"url":417,"description":418,"useCases":419,"indexable":185},"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.",72,{"id":421,"label":422,"issuer":423,"region":127,"url":424,"description":425,"useCases":426,"indexable":185},"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.",50,{"id":428,"label":429,"issuer":430,"region":431,"url":432,"description":433,"useCases":377,"indexable":185},"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.",{"id":435,"label":436,"issuer":437,"region":431,"url":438,"description":439,"useCases":440,"indexable":185},"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":442,"label":443,"issuer":126,"region":127,"url":444,"description":445,"useCases":446,"indexable":185},"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":448,"label":449,"issuer":450,"region":395,"url":451,"description":452,"useCases":446,"indexable":185},"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":454,"label":455,"issuer":456,"region":403,"url":457,"description":458,"useCases":459,"indexable":185},"hipaa","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.",21,{"id":461,"label":462,"issuer":463,"region":403,"url":464,"description":465,"useCases":459,"indexable":185},"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":467,"label":468,"issuer":469,"region":127,"url":470,"description":471,"useCases":472,"indexable":185},"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":474,"label":475,"issuer":476,"region":403,"url":477,"description":478,"useCases":479,"indexable":185},"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.",17,{"id":481,"label":482,"issuer":126,"region":127,"url":483,"description":484,"useCases":485,"indexable":185},"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.",14,{"id":487,"label":488,"issuer":489,"region":395,"url":490,"description":491,"useCases":485,"indexable":185},"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":493,"label":494,"issuer":126,"region":127,"url":495,"description":496,"useCases":485,"indexable":185},"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":498,"label":499,"issuer":500,"region":395,"url":501,"description":502,"useCases":485,"indexable":185},"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":504,"label":505,"issuer":126,"region":127,"url":506,"description":507,"useCases":288,"indexable":185},"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.",{"id":509,"label":510,"issuer":126,"region":127,"url":511,"description":512,"useCases":288,"indexable":185},"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":514,"label":515,"issuer":516,"region":403,"url":517,"description":518,"useCases":519,"indexable":185},"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.",12,{"id":521,"label":522,"issuer":126,"region":127,"url":523,"description":524,"useCases":519,"indexable":185},"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":526,"label":527,"issuer":126,"region":127,"url":528,"description":529,"useCases":530,"indexable":185},"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.",11,{"id":532,"label":533,"issuer":534,"region":403,"url":535,"description":536,"useCases":537,"indexable":185},"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.",10,{"id":539,"label":540,"issuer":541,"region":127,"url":542,"description":543,"useCases":544,"indexable":185},"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.",9,{"id":546,"label":547,"issuer":126,"region":127,"url":548,"description":549,"useCases":544,"indexable":185},"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":551,"label":552,"issuer":430,"region":431,"url":553,"description":554,"useCases":544,"indexable":185},"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.",{"id":556,"label":557,"issuer":558,"region":403,"url":559,"description":560,"useCases":561,"indexable":185},"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.",7,{"id":563,"label":564,"issuer":126,"region":127,"url":565,"description":566,"useCases":64,"indexable":185},"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":568,"label":569,"issuer":570,"region":127,"url":571,"description":572,"useCases":64,"indexable":185},"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.",{"id":574,"label":575,"issuer":576,"region":431,"url":577,"description":578,"useCases":321,"indexable":185},"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":580,"label":581,"issuer":582,"region":583,"url":584,"description":585,"useCases":321,"indexable":185},"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.",{"id":587,"label":588,"issuer":430,"region":431,"url":589,"description":590,"useCases":321,"indexable":185},"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":592,"label":593,"issuer":594,"region":127,"url":595,"description":596,"useCases":321,"indexable":185},"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":126,"region":127,"url":600,"description":601,"useCases":322,"indexable":185},"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":603,"label":604,"issuer":605,"region":403,"url":606,"description":607,"useCases":322,"indexable":185},"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.",1794572849694]