[{"data":1,"prerenderedAt":635},["ShallowReactive",2],{"uc-ambient-clinical-documentation":3,"uc-regulations":427},{"useCase":4,"evidence":196,"blitsAiDeployments":305,"benchmarks":306,"indicative":331,"related":334,"indexability":425,"includeUnpublished":202},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":26,"audience":29,"autonomy":30,"adoptionStage":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":40,"indicativeValue":48,"macroEstimates":82,"feasibility":83,"implementation":96,"risk":139,"blitsAi":177,"faq":179,"related":189,"datePublished":191,"dateModified":191,"lastVerified":191,"changelog":192,"slug":195},"AI ambient scribe for clinical documentation","Ambient clinical documentation","Ambient AI scribes for clinical documentation","Ambient AI scribes draft the clinical note from the visit for the clinician to review. Permanente physicians used them in more than 2.5 million patient encounters.","published","An AI scribe that listens, with the patient's consent, to the conversation between a clinician and a patient and drafts the clinical note, and often the letter or after visit summary, for the clinician to review, edit and sign in the health record. It documents; it does not diagnose or decide on treatment.",[12,13,14,15,16],"ambient AI scribe","AI medical scribe","ambient voice technology","ambient listening for clinical notes","AI clinical note taking",[18],"healthcare",[20,21],"operations","knowledge-management",[23,24,25],"speech-analytics","summarization","content-generation",[27,28],"internal-tools","mobile-app","employee-facing","copilot","mainstream","Clinicians spend a large share of every working day on documentation. During the visit they type\nwhile the patient talks, and after clinic they finish notes and letters in the evening, the time\nclinicians call \"pajama time\". Patients notice the screen between them and their doctor, and health\nsystems that deploy scribes name the documentation load as a driver of clinician burnout.\n\nHuman scribes and dictation help, but they are expensive or still take clinician time. Generative\nAI changed the economics: speech recognition that copes with a real consultation, followed by a\nlanguage model that turns the conversation into a structured note in the clinician's preferred\nformat. The risk moved with it. A fluent note that contains something nobody said, or leaves out a\nsymptom, ends up in the medical record unless the clinician catches it.",[],"1. **Ask for consent.** The clinician tells the patient that an AI scribe will listen and records\n   the consent; the patient can decline or stop it at any time, even mid visit.\n2. **Capture the conversation.** A phone, tablet or workstation app records the visit (in person,\n   phone or video) and streams it to speech recognition with speaker separation.\n3. **Draft the note.** A language model turns the transcript into a note in the clinician's template\n   and specialty format (history, examination, assessment and plan), and optionally a letter,\n   patient instructions or suggested codes.\n4. **Review and sign.** The draft appears in the health record or next to it; the clinician checks\n   it against what happened, edits it and signs it. Nothing enters the record unreviewed.\n5. **Learn from edits.** Edit rates, clinician feedback and quality samples show where the drafts\n   are weak, per specialty and per template.",[36,37,38,39],"employee-productivity","customer-experience","speed","cost-to-serve",[41,42,43,44,45,46,47],"time-saved-per-task","hours-saved","handling-time-reduction","productivity-gain","users-served","interactions-handled","employee-adoption",{"referenceOrg":49,"inputs":50,"formula":77,"currency":78,"period":79,"resultLabel":80,"caveat":81},"A health system with 1,000 clinicians using an ambient scribe",[51,56,63,70],{"key":52,"label":53,"low":54,"high":54,"unit":52,"note":55},"clinicians","Clinicians using the scribe",1000,"The reference health system.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"encountersPerClinician","Documented encounters per clinician per year",2000,3000,"encounters per clinician per year","Editorial assumption for outpatient and primary care clinicians. Replace with your own visit volumes.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"minutesSaved","Documentation minutes saved per encounter",0.2,0.5,"minutes per encounter","Derived from The Permanente Medical Group analysis on this page, with two assumptions of our own: a working day of 8 hours, and that the saving of 1,794 working days \"in one year\" can be set against the more than 2.5 million encounters counted over the 63 week evaluation. That gives about 0.34 minutes per encounter on average (about 0.42 if the encounters are scaled to 52 weeks). The top third of users accounted for 89% of activations, and high users saved two and a half times more per note than infrequent users, so the average is close to what frequent users saved (roughly 0.35 to 0.45 minutes); occasional users saved less. The range stays around that evidence. Replace with your own time data.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"costPerHour","Fully loaded cost per clinician hour",100,150,"USD per hour","Editorial assumption. Replace with your own fully loaded clinician cost.","clinicians * encountersPerClinician * minutesSaved / 60 * costPerHour","USD","per year","Clinician documentation time released","Time released is not cash saved unless it becomes extra appointments or less overtime. The figure leaves out licence and integration costs, clinician review time for drafts, the effect on burnout and retention, and any change in coding completeness.",[],{"complexity":84,"complexityNote":85,"dataPrerequisites":86,"integrations":91},"medium","Mature products exist, so the work is in integration with the health record, consent and information governance, clinical safety assessment, device and network setup in clinics, and training clinicians to review drafts properly. Specialty templates and languages take tuning.",[87,88,89,90],"Note templates and documentation standards per specialty","A consent process and patient information text","A clinical safety case and a data protection impact assessment","A sample of real consultations (with consent) to test draft quality per specialty",[92,93,94,95],"Electronic health record for patient context and filing the signed note","Clinician devices (mobile app, desktop, dictation hardware)","Identity and single sign on for clinicians","Audit logging and records retention for audio and transcripts",{"steps":97,"guardrails":113,"humanInTheLoop":119,"kpisToInstrument":120,"failureModes":126},[98,101,104,107,110],{"title":99,"detail":100},"Start with willing clinicians in a few specialties","At The Permanente Medical Group in Northern California, mental health, emergency medicine and primary care doctors were the most likely to use the scribe. Pick specialties with long conversations and heavy notes, and clinicians who want the tool.",{"title":102,"detail":103},"Settle consent, retention and safety before go live","Decide how consent is asked and recorded, whether audio is kept or deleted after the note is signed, and complete the clinical safety assessment and data protection impact assessment.",{"title":105,"detail":106},"Tune templates per specialty","Build note formats with clinicians in each specialty and let individual clinicians adjust style, so the draft needs editing rather than rewriting.",{"title":108,"detail":109},"Train clinicians to review, not to trust","Teach what the tool gets wrong (medication names, negations, who said what) and make clear that the signature means the clinician has checked the note.",{"title":111,"detail":112},"Measure time and quality, then widen","Track documentation time, after hours time, edit rates and patient feedback against a baseline, and expand to more specialties once quality holds.",[114,115,116,117,118],"No note enters the record without clinician review and signature","Patient consent recorded for every encounter, with an easy way to decline or stop","The scribe documents only; it does not suggest diagnoses or place orders without clinician action","Audio and transcripts retained only as long as policy allows, with access logging","Health data processed in approved regions under HIPAA or GDPR special category rules","The clinician reviews, edits and signs every note and remains accountable for the record. A clinical safety officer owns the risk log, and a quality team samples signed notes against transcripts to find omissions and invented content.",[121,122,123,124,125],"Documentation time per encounter and after hours time in the record, before and after","Share of encounters where the scribe is used, per clinician and specialty","Edit distance between draft and signed note","Omissions and invented content found in quality samples","Patient consent and decline rates, and patient feedback",[127,130,133,136],{"title":128,"detail":129},"Invented or misattributed content","The draft contains a symptom, medication or statement nobody said, or attributes the patient's words to the clinician. Sample notes against transcripts and teach clinicians what to check.",{"title":131,"detail":132},"Automation complacency","Clinicians sign drafts after a glance because they are usually right. Measure review time and edit rates and make quality feedback visible.",{"title":134,"detail":135},"Low use after launch","Most of the time savings go to frequent users; occasional users gain little. Support adoption with training and specialty templates rather than counting licences.",{"title":137,"detail":138},"Consent that is not real","Patients are not told clearly or feel they cannot refuse. Script the consent, make declining easy and track decline rates.",{"euAiAct":140,"regulations":143,"guidance":150,"controls":166,"incidents":172},{"tier":141,"basis":142},"context-dependent","A scribe that only transcribes and summarises for a clinician to review is not listed in Annex III and is usually minimal risk, although the provider of a system that generates text can still owe the Article 50(2) duty to mark output as AI generated, unless an exception such as an assistive function for standard editing applies. If the product qualifies as medical device software under the EU Medical Device Regulation and needs a notified body assessment, for example because it suggests diagnoses or treatment, it becomes high risk under Article 6(1) and Annex I. Health data in audio and notes falls under GDPR Article 9 in every case.",[144,145,146,147,148,149],"eu-ai-act","gdpr","uk-gdpr","hipaa","nist-ai-rmf","iso-42001",[151,157,162],{"title":152,"issuer":153,"region":154,"url":155,"note":156},"MHRA clarifies regulatory status of ambient voice technologies used in the NHS","Medicines and Healthcare products Regulatory Agency","europe","https://www.gov.uk/government/news/mhra-clarifies-regulatory-status-of-ambient-voice-technologies-used-in-the-nhs","Confirms that products used solely for transcription, summarising consultations, drafting letters or suggesting codes for clinician review are not regulated as medical devices in Great Britain, while products that support diagnosis or treatment, or act without clinician review, are.",{"title":158,"issuer":159,"region":154,"url":160,"note":161},"Article 6, classification rules for high risk AI systems","European Union","https://artificialintelligenceact.eu/article/6/","An AI system that is, or is a safety component of, a product covered by EU harmonisation legislation such as the Medical Device Regulation and needs third party conformity assessment is high risk.",{"title":163,"issuer":159,"region":154,"url":164,"note":165},"Regulation (EU) 2017/745 on medical devices","https://eur-lex.europa.eu/eli/reg/2017/745/oj","Decides whether a scribe with clinical functions is medical device software, and its risk class.",[167,168,169,170,171],"Clinical safety case and hazard log for the scribe, owned by a named clinical safety officer","Data protection impact assessment covering audio, transcripts and vendor processing","Consent procedure and patient information in plain language","Audit trail of drafts, edits and signatures per note","Periodic quality sampling of signed notes against source audio or transcripts",[173],{"title":174,"url":175,"note":176},"OpenAI's transcription tool hallucinates more than any other, experts say, but hospitals keep using it","https://fortune.com/2024/10/26/openai-transcription-tool-whisper-hallucination-rate-ai-tools-hospitals-patients-doctors/","An Associated Press investigation reported that the Whisper speech model can invent text, and that a Whisper based medical transcription tool from Nabla, used by over 30,000 clinicians and 40 health systems, deletes the original audio, so transcripts cannot be checked against the recording. Nabla said clinicians must edit and approve notes.",{"howToBuild":178},"On Blits.ai the documentation step is an **agentic workflow** that the organization's clinic app\ntriggers through the **REST API** after each consented visit. The audio is transcribed with\n**self hosted transcription and speaker diarization** (WhisperX), which keeps \"who spoke when\" and\ncan run on Blits.ai infrastructure for data sovereignty, or with one of the supported speech to text\nproviders. WhisperX is based on Whisper, the model family in the incident on this page, so keep the\naudio or transcript available for checking drafts.\nAn **AI agent** with **structured output** turns the transcript into the note sections of the\nchosen template, and **custom functions** return the draft to the record system for review.\n\nThe clinician reviews and signs the note in the health record, not in Blits.ai; the workflow only\nreturns a draft. **PII masking** limits the health\ndata that reaches a model, **audit trails** record every run, and **test suites** grade drafts\nagainst reviewed notes per specialty before each change goes live. The platform is model agnostic,\nand EU and UAE data residency keeps audio and notes in region.",[180,183,186],{"question":181,"answer":182},"How much time do ambient AI scribes save?","The Permanente Medical Group in Northern California reports that AI scribes saved its physicians the equivalent of 1,794 working days in one year; over a 63 week evaluation they were used in more than 2.5 million encounters, and high users saved two and a half times more time per note than infrequent users. In an NHS England sponsored study led by Great Ormond Street Hospital, appointments were 8.2% shorter and A&E clinicians saw 13.4% more patients per shift.",{"question":184,"answer":185},"Is an AI scribe a medical device?","It depends on what it does. The UK MHRA confirmed in July 2026 that tools used solely to transcribe, summarise, draft letters or suggest codes for clinician review are not medical devices, while tools that support diagnosis or treatment, or act without review, are. In the EU, a scribe that qualifies as medical device software under the Medical Device Regulation and needs a notified body is also high risk under the AI Act.",{"question":187,"answer":188},"What are the main safety risks?","Invented content, omissions and misattributed statements in a note that looks complete. Keep clinician review and signature mandatory, sample signed notes against transcripts, and be careful with tools that delete the audio before anyone can check the transcript.",[190],"medical-coding-automation","2026-09-27",[193],{"date":191,"note":194},"First published","ambient-clinical-documentation",[197,233,274],{"title":198,"useCases":199,"organization":200,"vendors":205,"summary":211,"stage":212,"year":213,"channels":214,"languages":215,"metrics":217,"outcomeDisclosed":202,"sources":218,"verification":228,"grade":230,"id":231,"organizationSlug":232},"Veterans Health Administration: Ambient Scribe for all primary care providers",[195],{"name":201,"anonymized":202,"country":203,"region":204,"industry":18},"US Department of Veterans Affairs, Veterans Health Administration",false,"US","north-america",[206,209],{"name":207,"role":208},"Abridge","platform",{"name":210,"role":208},"Knowtex","The 2025 federal AI use case inventory lists Abridge and Knowtex ambient scribe pilots at VA, both flagged as high impact. As of June 2026 VHA has deployed Ambient Scribe to all Patient Aligned Care Team primary care providers, including physicians, physician assistants and advanced practice nurses, after a phased rollout that began at 10 VA medical centers. With the Veteran's verbal consent the tool drafts the progress note from the conversation; the provider reviews and edits it before signing it into the record, and Veterans can opt out at any time, even mid visit. VA plans to extend it to selected outpatient specialty care. No outcome figures are published by VA.","scaled",2026,[27],[216],"en",[],[219,224],{"url":220,"title":221,"publisher":222,"date":223},"https://news.va.gov/148010/ambient-scribe-reimagining-va-clinic-experience/","Ambient Scribe reimagines the VA clinic experience","VA News","2026-07-29",{"url":225,"title":226,"publisher":227},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 federal agency AI use case inventory, individually reported use cases (raw data)","Office of Management and Budget (GitHub)",{"level":229,"checkedAt":191},"source-verified","B","veterans-health-administration-ambient-scribe",null,{"title":234,"useCases":235,"organization":236,"vendors":239,"summary":242,"stage":243,"year":244,"channels":245,"languages":246,"metrics":247,"outcomeDisclosed":266,"sources":267,"verification":272,"grade":230,"id":273,"organizationSlug":232},"Great Ormond Street Hospital: London wide trial of an AI scribe across nine NHS sites",[195],{"name":237,"anonymized":202,"country":238,"region":154,"industry":18},"Great Ormond Street Hospital for Children NHS Foundation Trust","GB",[240],{"name":241,"role":208},"TORTUS","An NHS England sponsored study led by the GOSH DRIVE innovation unit tested the TORTUS ambient scribe at nine London sites, including hospitals, GP practices, mental health services and ambulance teams, over more than 17,000 patient encounters. The tool transcribes the consultation and drafts a clinic note and letter that the clinician checks and edits before saving. Direct patient interaction time rose and appointments got shorter; in A&E at St George's University Hospital, clinicians saw more patients per shift. A rollout across GOSH outpatient settings was planned to follow.","pilot",2025,[27],[216],[248,256,262],{"kpi":46,"value":249,"unit":250,"qualifier":251,"period":252,"claimant":253,"quote":254,"sourceUrl":255},17000,"count","at-least","evaluation across nine London NHS sites","organization","Over 17,000 patient encounters were evaluated across a diverse range of sites including hospitals, GP practices, mental health services and ambulance teams.","https://www.gosh.nhs.uk/news/researchgosh-led-trial-of-ai-scribe-technology-shows-transformative-benefits-for-patients-and-clinicians-across-london/",{"kpi":43,"value":257,"unit":258,"qualifier":259,"period":260,"claimant":253,"quote":261,"sourceUrl":255},8.2,"percent","exact","overall appointment length, trial sites","Results showed a 23.5% increase in direct patient interaction time during appointments, alongside an 8.2% reduction in overall appointment length when AI-scribes were used.",{"kpi":44,"value":263,"unit":258,"qualifier":259,"period":264,"claimant":253,"quote":265,"sourceUrl":255},13.4,"A&E at St George's University Hospital, patients seen per shift","A&E saw particularly strong results, with a 13.4% increase in patients seen per shift.",true,[268],{"url":255,"title":269,"publisher":270,"date":271},"GOSH-led trial of AI-scribe technology shows 'transformative' benefits for patients and clinicians across London","Great Ormond Street Hospital","2025-09-04",{"level":229,"checkedAt":191},"great-ormond-street-hospital-ai-scribe-trial",{"title":275,"useCases":276,"organization":277,"vendors":279,"summary":281,"stage":212,"year":282,"channels":283,"languages":284,"metrics":285,"outcomeDisclosed":266,"sources":294,"verification":303,"grade":230,"id":304,"organizationSlug":232},"Kaiser Permanente: ambient AI scribes for physicians and clinicians",[195],{"name":278,"anonymized":202,"country":203,"region":204,"industry":18},"Kaiser Permanente",[280],{"name":207,"role":208},"Kaiser Permanente made an ambient documentation tool from Abridge available to doctors and other clinicians at its 40 hospitals and more than 600 medical offices in August 2024, after a year of testing. With the patient's consent, the tool listens to the visit and drafts the clinical note, which the clinician reviews before it enters the record. An analysis by The Permanente Medical Group in Northern California, published in NEJM Catalyst, found that the scribes saved the equivalent of 1,794 working days in one year, and that time savings were concentrated among the most frequent users. The tool does not make decisions or recommendations about care.",2024,[27],[216],[286,291],{"kpi":45,"value":287,"unit":250,"qualifier":259,"period":288,"claimant":253,"quote":289,"sourceUrl":290},7260,"The Permanente Medical Group, 63 week evaluation period","AI scribes were used by 7,260 Permanente physicians in more than 2.5 million patient encounters during the evaluation period.","https://permanente.org/analysis-ai-scribes-save-physicians-time-improve-patient-interactions-and-work-satisfaction/",{"kpi":46,"value":292,"unit":250,"qualifier":251,"period":293,"claimant":253,"quote":289,"sourceUrl":290},2500000,"The Permanente Medical Group, 63 week evaluation period, patient encounters",[295,299],{"url":290,"title":296,"publisher":297,"date":298},"Analysis: AI scribes save physicians time, improve patient interactions and work satisfaction","The Permanente Medical Group","2025-04-07",{"url":300,"title":301,"publisher":278,"date":302},"https://about.kaiserpermanente.org/news/press-release-archive/kaiser-permanente-improves-member-experience-with-ai-enabled-clinical-technology","Kaiser Permanente improves member experience with AI-enabled clinical technology","2024-08-14",{"level":229,"checkedAt":191},"kaiser-permanente-ambient-ai-scribes",0,[307,315,321,326],{"kpi":46,"label":308,"unit":250,"aggregate":202,"higherIsBetter":266,"n":309,"nUpTo":305,"median":310,"min":249,"max":292,"byClaimant":311,"vendorOnly":202,"points":312},"Interactions handled",2,1258500,{"organization":309,"vendor":305,"regulator":305,"independent":305},[313,314],{"evidenceId":304,"organization":278,"value":292,"qualifier":251,"claimant":253,"grade":230,"pooled":266},{"evidenceId":273,"organization":237,"value":249,"qualifier":251,"claimant":253,"grade":230,"pooled":266},{"kpi":43,"label":316,"unit":258,"aggregate":266,"higherIsBetter":266,"n":317,"nUpTo":305,"median":257,"min":257,"max":257,"byClaimant":318,"vendorOnly":202,"points":319},"Handling time reduction",1,{"organization":317,"vendor":305,"regulator":305,"independent":305},[320],{"evidenceId":273,"organization":237,"value":257,"qualifier":259,"claimant":253,"grade":230,"pooled":266},{"kpi":44,"label":322,"unit":258,"aggregate":266,"higherIsBetter":266,"n":317,"nUpTo":305,"median":263,"min":263,"max":263,"byClaimant":323,"vendorOnly":202,"points":324},"Productivity gain",{"organization":317,"vendor":305,"regulator":305,"independent":305},[325],{"evidenceId":273,"organization":237,"value":263,"qualifier":259,"claimant":253,"grade":230,"pooled":266},{"kpi":45,"label":327,"unit":250,"aggregate":202,"higherIsBetter":266,"n":317,"nUpTo":305,"median":287,"min":287,"max":287,"byClaimant":328,"vendorOnly":202,"points":329},"Users served",{"organization":317,"vendor":305,"regulator":305,"independent":305},[330],{"evidenceId":304,"organization":278,"value":287,"qualifier":259,"claimant":253,"grade":230,"pooled":266},{"low":332,"high":333},666666.6666666667,3750000,[335,357,376,401],{"slug":190,"title":336,"shortTitle":337,"definition":338,"status":9,"industries":339,"functions":340,"patterns":342,"audience":345,"autonomy":346,"adoptionStage":347,"evidenceCount":348,"publicEvidenceCount":348,"organizations":349,"bestGrade":230,"headline":352,"lastVerified":191,"indexable":266},"AI medical coding for clinical encounters","Medical coding automation","AI that reads the clinical documentation of an encounter and assigns the diagnosis and procedure codes (such as ICD-10, CPT and HCPCS) needed for billing and reporting, either as suggestions for a certified coder or autonomously for encounters it can code with high confidence, sending the rest to coders with the reasons.",[18],[341,20],"finance-and-accounting",[343,344],"classification-and-routing","document-processing","back-office","supervised-agent","early-adopters",3,[350,201,351],"Mass General Brigham","Your Health",{"kpi":353,"label":354,"unit":258,"n":317,"nUpTo":305,"kind":355,"value":356,"qualifier":259,"claimant":253,"organization":351,"vendorReported":202},"accuracy","Accuracy","reported",98.3,{"slug":358,"title":359,"shortTitle":360,"definition":361,"status":9,"industries":362,"functions":367,"patterns":368,"audience":29,"autonomy":30,"adoptionStage":31,"evidenceCount":369,"publicEvidenceCount":369,"organizations":370,"bestGrade":230,"headline":232,"lastVerified":191,"indexable":266},"meeting-summarization-and-action-items","AI meeting summarization and action items","Meeting summaries and action items","AI that summarizes internal and operational meetings, such as team, project, board and case meetings: it transcribes an online or in person meeting with the participants' knowledge and produces a summary, decisions and action items with owners and dates for the organizer to check and share. It is the general purpose tool; client advice meetings and sales calls, which feed a regulated record or a sales pipeline, have their own pages.",[363,364,365,366],"cross-industry","government","technology","professional-services",[21,20],[24,23],5,[371,372,373,374,375],"U.S. Department of Labor","Ministry of Justice","Softcat","Trace3","Government Digital Service",{"slug":377,"title":378,"shortTitle":379,"definition":380,"status":9,"industries":381,"functions":384,"patterns":385,"audience":345,"autonomy":30,"adoptionStage":31,"evidenceCount":387,"publicEvidenceCount":369,"organizations":388,"bestGrade":394,"headline":395,"lastVerified":400,"indexable":266},"audio-and-video-transcription-and-captioning","AI transcription, subtitles and captions for audio and video","Transcription and captioning","AI that transcribes recorded audio and video, such as podcasts, broadcasts, lessons, interviews and hearings, in several languages, separates the speakers and produces timed transcripts, subtitles and captions for a human editor to check, delivered as files for publishing or the archive.",[382,383,363],"media-and-entertainment","education",[20,21],[23,386,25],"translation",6,[389,390,391,392,393],"Ateme","Comeen","Pacers Sports & Entertainment","Sveriges Television (SVT)","Warner Bros. Discovery","C",{"kpi":396,"label":397,"unit":258,"n":317,"nUpTo":305,"kind":355,"value":398,"qualifier":259,"claimant":399,"organization":393,"vendorReported":266},"cost-reduction","Cost reduction",50,"vendor","2026-09-26",{"slug":402,"title":403,"shortTitle":404,"definition":405,"status":9,"industries":406,"functions":411,"patterns":413,"audience":29,"autonomy":415,"adoptionStage":31,"evidenceCount":416,"publicEvidenceCount":369,"organizations":417,"bestGrade":230,"headline":423,"lastVerified":191,"indexable":266},"live-agent-assist","Real time AI assist for contact centre agents","Live agent assist","A real time copilot for human contact centre agents during a live call or chat: it transcribes the conversation as it happens, surfaces the relevant knowledge and next step, drafts responses, and writes the after call summary and CRM notes, while the agent stays in control of what is said and done.",[363,407,408,409,18,410,365],"banking","insurance","telecommunications","retail-and-ecommerce",[412,20],"customer-service",[23,414,24,25],"rag-knowledge-assistant","assist",7,[418,419,420,421,422],"DBS Bank","Definity","Oportun","SEB","SIGNAL IDUNA",{"kpi":44,"label":322,"unit":258,"n":309,"nUpTo":305,"kind":355,"value":424,"qualifier":259,"claimant":399,"organization":419,"vendorReported":266},15,{"indexable":266,"reasons":426},[],[428,433,438,445,451,457,463,470,478,485,492,498,505,511,517,522,529,535,540,546,552,558,564,569,574,581,588,593,598,605,612,618,624,629],{"id":144,"label":429,"issuer":159,"region":154,"url":430,"description":431,"useCases":432,"indexable":266},"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":145,"label":434,"issuer":159,"region":154,"url":435,"description":436,"useCases":437,"indexable":266},"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":149,"label":439,"issuer":440,"region":441,"url":442,"description":443,"useCases":444,"indexable":266},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":148,"label":446,"issuer":447,"region":204,"url":448,"description":449,"useCases":450,"indexable":266},"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":452,"label":453,"issuer":159,"region":154,"url":454,"description":455,"useCases":456,"indexable":266},"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":146,"label":458,"issuer":459,"region":154,"url":460,"description":461,"useCases":462,"indexable":266},"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":464,"label":465,"issuer":466,"region":154,"url":467,"description":468,"useCases":469,"indexable":266},"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":471,"label":472,"issuer":473,"region":474,"url":475,"description":476,"useCases":477,"indexable":266},"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":479,"label":480,"issuer":481,"region":474,"url":482,"description":483,"useCases":484,"indexable":266},"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":486,"label":487,"issuer":488,"region":441,"url":489,"description":490,"useCases":491,"indexable":266},"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":493,"label":494,"issuer":495,"region":204,"url":496,"description":497,"useCases":491,"indexable":266},"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":499,"label":500,"issuer":501,"region":154,"url":502,"description":503,"useCases":504,"indexable":266},"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":506,"label":507,"issuer":508,"region":441,"url":509,"description":510,"useCases":424,"indexable":266},"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.",{"id":512,"label":513,"issuer":159,"region":154,"url":514,"description":515,"useCases":516,"indexable":266},"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":518,"label":519,"issuer":159,"region":154,"url":520,"description":521,"useCases":516,"indexable":266},"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":523,"label":524,"issuer":525,"region":204,"url":526,"description":527,"useCases":528,"indexable":266},"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":530,"label":531,"issuer":159,"region":154,"url":532,"description":533,"useCases":534,"indexable":266},"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":147,"label":536,"issuer":537,"region":204,"url":538,"description":539,"useCases":534,"indexable":266},"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":541,"label":542,"issuer":543,"region":441,"url":544,"description":545,"useCases":534,"indexable":266},"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":547,"label":548,"issuer":159,"region":154,"url":549,"description":550,"useCases":551,"indexable":266},"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":553,"label":554,"issuer":555,"region":204,"url":556,"description":557,"useCases":551,"indexable":266},"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":559,"label":560,"issuer":473,"region":474,"url":561,"description":562,"useCases":563,"indexable":266},"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.",10,{"id":565,"label":566,"issuer":159,"region":154,"url":567,"description":568,"useCases":563,"indexable":266},"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":570,"label":571,"issuer":159,"region":154,"url":572,"description":573,"useCases":563,"indexable":266},"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":575,"label":576,"issuer":577,"region":154,"url":578,"description":579,"useCases":580,"indexable":266},"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":582,"label":583,"issuer":584,"region":204,"url":585,"description":586,"useCases":587,"indexable":266},"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.",8,{"id":589,"label":590,"issuer":159,"region":154,"url":591,"description":592,"useCases":587,"indexable":266},"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":594,"label":595,"issuer":159,"region":154,"url":596,"description":597,"useCases":387,"indexable":266},"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":599,"label":600,"issuer":601,"region":602,"url":603,"description":604,"useCases":369,"indexable":266},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":606,"label":607,"issuer":608,"region":154,"url":609,"description":610,"useCases":611,"indexable":266},"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":613,"label":614,"issuer":615,"region":154,"url":616,"description":617,"useCases":611,"indexable":266},"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":619,"label":620,"issuer":621,"region":474,"url":622,"description":623,"useCases":348,"indexable":266},"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":625,"label":626,"issuer":159,"region":154,"url":627,"description":628,"useCases":348,"indexable":266},"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":630,"label":631,"issuer":632,"region":204,"url":633,"description":634,"useCases":348,"indexable":266},"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.",1790598296197]