[{"data":1,"prerenderedAt":593},["ShallowReactive",2],{"uc-clinical-trial-patient-matching":3,"uc-regulations":383},{"useCase":4,"evidence":184,"blitsAiDeployments":278,"benchmarks":279,"indicative":299,"related":302,"indexability":381,"includeUnpublished":190},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":22,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"problem":30,"problemStats":31,"howItWorks":37,"valueDrivers":38,"kpis":42,"indicativeValue":47,"macroEstimates":82,"feasibility":83,"implementation":95,"risk":138,"blitsAi":165,"faq":167,"related":177,"datePublished":179,"dateModified":179,"lastVerified":179,"changelog":180,"slug":183},"AI clinical trial patient matching and prescreening","Clinical trial patient matching","AI clinical trial patient matching","AI checks records against trial criteria; staff confirm. Yale Cancer Center cut screening time per reviewed chart by 41%; Mount Sinai runs AI matching systemwide.","published","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.",[12,13,14,15],"AI trial prescreening","clinical trial recruitment AI","patient to trial matching","eligibility screening with language models",[17,18],"healthcare","pharma-and-life-sciences",[20,21],"operations","analytics-and-reporting",[23,24],"document-processing","classification-and-routing",[26],"internal-tools","employee-facing","assist","early-adopters","Many clinical trials struggle to enroll enough patients, and slow enrollment extends trial timelines.\nEligibility criteria are long and specific (biomarkers, prior treatments, lab values, stage), and\nthe facts needed to check them are scattered across notes, pathology reports and lab results.\nResearch coordinators screen charts by hand, one trial and one patient at a time, so they see only a\nfraction of the patients who might qualify, and patients treated outside the flagship hospital are\nconsidered less often.\n\nThe result is lost opportunity on both sides: patients are not offered trials that could help them,\nsponsors wait longer for results, and trial participation stays concentrated at flagship academic sites.\nLanguage models can read clinical notes at scale, but eligibility errors in either direction matter,\nso the design has to keep people in charge of the final decision.",[32],{"statement":33,"sourceTitle":34,"sourceUrl":35,"year":36},"Researchers at Yale Cancer Center write that cancer clinical trial enrollment remains critically low at 5% to 7% of adult patients, despite exponential growth in the number of available trials.","Clinical Trial Patient Matching: A Real-Time, Common Data Model and Artificial Intelligence-Driven System for Semiautomated Patient Prescreening in Cancer Clinical Trials","https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:41512229%20AND%20SRC:MED&resultType=core&format=json",2026,"1. **Encode the criteria.** Each trial's inclusion and exclusion criteria are turned into checks,\n   some structured (age, diagnosis codes, lab values) and some that need the notes (prior lines of\n   therapy, performance status, biomarkers).\n2. **Find the population.** Rules on structured data narrow the whole patient population to\n   candidates, for example patients with a relevant diagnosis and an upcoming visit.\n3. **Read the record.** A language model or NLP pipeline reads notes and reports for each candidate\n   and marks each criterion as met, not met or unknown, with the passage that supports it.\n4. **Present a ranked list.** Research staff and treating clinicians see likely eligible patients and\n   the evidence, ideally before the patient's next visit.\n5. **Confirm and invite.** Staff verify eligibility in the record, discuss the trial with the treating\n   clinician and approach the patient; outcomes feed back to improve the criteria and the model.",[39,40,41],"inclusion-and-access","speed","employee-productivity",[43,44,45,46],"handling-time-reduction","interactions-handled","accuracy","detection-rate-improvement",{"referenceOrg":48,"inputs":49,"formula":77,"currency":78,"period":79,"resultLabel":80,"caveat":81},"A cancer center whose research staff prescreen 15,000 charts a year",[50,56,63,70],{"key":51,"label":52,"low":53,"high":53,"unit":54,"note":55},"charts","Charts prescreened by hand per year",15000,"charts per year","Editorial assumption for the reference cancer center, replace with your own.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"minutesPerChart","Minutes of manual review per chart",3,15,"minutes per chart","Yale Cancer Center measured 3.1 minutes per chart for its prescreening workflow. Cleveland Clinic writes that a manual chart review can take more than 30 minutes per record, depending on the complexity of the criteria and the volume of history; the high value is an editorial assumption well below that. Replace with your own time data.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"reviewAvoided","Share of manual chart review avoided",0.4,0.8,"fraction of review minutes","Conservative against Yale Cancer Center's report of a tenfold reduction in chart review workload and 41% less screening time per chart that was still reviewed.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"costPerHour","Fully loaded cost per research coordinator hour",40,70,"USD per hour","Editorial assumption. Replace with your own.","charts * minutesPerChart / 60 * reviewAvoided * costPerHour","USD","per year","Research staff screening time released","Screening effort only, and likely small next to the main value: more patients offered trials and faster enrollment, neither of which is included. It also leaves out the cost of encoding criteria, integrating the record and validating the tool.",[],{"complexity":84,"complexityNote":85,"dataPrerequisites":86,"integrations":91},"medium","Reading the record is feasible with current models; the effort goes into access to structured and unstructured data (often through a common data model such as OMOP), turning free text criteria into checks for each new trial, and fitting the output into how research teams already work.",[87,88,89,90],"Access to structured record data and clinical notes, pathology and lab reports","Trial protocols with inclusion and exclusion criteria, and a list of open trials","Visit schedules to time outreach before appointments","Past screening decisions to measure accuracy",[92,93,94],"Electronic health record or clinical data warehouse (for example on the OMOP model)","Clinical trial management system for trials and enrollment status","Research staff worklists and secure messaging to treating clinicians",{"steps":96,"guardrails":112,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":125},[97,100,103,106,109],{"title":98,"detail":99},"Start with trials that struggle to enroll","Pick a few open trials with clear criteria and slow accrual, and measure how many eligible patients the current process finds.",{"title":101,"detail":102},"Split criteria into structured and text checks","Use structured data to narrow the population cheaply and reserve language model reading for criteria that only the notes contain.",{"title":104,"detail":105},"Show evidence per criterion","For every criterion show met, not met or unknown and the passage behind it, so staff can verify in seconds rather than rereading the chart.",{"title":107,"detail":108},"Measure against manual screening","Compare accuracy, missed patients and time per chart with the manual process on the same trials, as Yale Cancer Center and Cleveland Clinic did, before scaling.",{"title":110,"detail":111},"Scale across trials and sites","Add trials and community sites, and check that patients at every site and in every group are identified at similar rates.",[113,114,115,116,117],"Eligibility is always confirmed by research staff or the investigator before a patient is approached","The treating clinician is involved before any patient contact","Evidence shown for every criterion, with unknowns marked rather than guessed","Access to records for prescreening limited to what research rules and local approvals allow","Identification rates monitored by site, sex, age and ethnicity to catch unequal access","Research coordinators and investigators decide who is eligible and who is approached, together with the treating clinician. The tool prioritizes and explains; it never enrolls or contacts a patient itself.",[120,121,122,123,124],"Eligible patients identified per trial per month, compared with manual screening","Screening minutes per chart and charts reviewed per enrollment","Accuracy of eligibility suggestions on a verified sample, including missed eligible patients","Enrollment and time to first patient per trial","Identification and enrollment rates by site and demographic group",[126,129,132,135],{"title":127,"detail":128},"Missed eligible patients","Criteria encoded too strictly, or facts hidden in scanned documents, exclude patients who qualify. Measure sensitivity against manual screening, not only precision.",{"title":130,"detail":131},"Confident but wrong eligibility","The model marks a criterion as met from an outdated or negated note. Show the source passage and date and require verification.",{"title":133,"detail":134},"More candidates, no more enrollments","Lists grow but staff and clinicians have no time to act. Fit the output to visit schedules and worklists and track enrollments, not matches.",{"title":136,"detail":137},"Conflicts of interest in evaluation","Health systems that invest in the vendor they evaluate may overstate results. Look for independent or prospective evaluations.",{"euAiAct":139,"regulations":142,"guidance":147,"controls":159,"incidents":164},{"tier":140,"basis":141},"context-dependent","Prescreening for research that staff verify is not listed in Annex III and is usually minimal risk. The Article 2(6) exclusion covers only systems developed and put into service for the sole purpose of scientific research and development, so an operational recruitment tool used across a health system usually falls inside the Act. If the software recommends trials to a clinician as a treatment option for an individual patient, it may qualify as medical device software under the Medical Device Regulation; where that needs a notified body assessment, it is high risk under Article 6(1). Processing health records for research falls under GDPR Article 9 and national research rules.",[143,144,145,146],"eu-ai-act","gdpr","hipaa","nist-ai-rmf",[148,154],{"title":149,"issuer":150,"region":151,"url":152,"note":153},"Reflection paper on the use of artificial intelligence (AI) in the medicinal product lifecycle","European Medicines Agency","europe","https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-use-artificial-intelligence-ai-medicinal-product-lifecycle_en.pdf","Says AI used in clinical trials should meet applicable ICH E6 good clinical practice requirements, and that where a use could have high regulatory impact or high patient risk and the method has not been previously qualified by the EMA for that context of use, the model documentation may be treated as clinical trial data and requested at marketing authorization, clinical trial application or GCP inspection. A reflection paper, not binding, and general to AI in clinical trials rather than specific to recruitment.",{"title":155,"issuer":156,"region":151,"url":157,"note":158},"Regulation (EU) No 536/2014 on clinical trials on medicinal products for human use","European Union","https://eur-lex.europa.eu/eli/reg/2014/536/oj","Sets the EU rules on trial conduct, informed consent and subject protection that recruitment processes supported by AI must respect.",[160,161,162,163],"Documented approval of prescreening under the institution's research governance and privacy rules","Versioned criteria per trial with an owner and review against protocol amendments","Audit trail of suggestions, staff decisions and patient contacts","Periodic accuracy and fairness review per trial and site",[],{"howToBuild":166},"On Blits.ai prescreening runs as a scheduled **agentic workflow**, for example each week for\npatients with upcoming visits. A clinical data warehouse on PostgreSQL is\nregistered as a **SQL knowledge base** so the agent can narrow the population with structured\nqueries (other databases are reached through custom functions), trial protocols sit in a\n**knowledge base** with hybrid retrieval, and **custom functions** fetch the notes and reports for\neach candidate. An **AI agent** with **structured output** marks each criterion as met, not met or\nunknown, with the supporting passage.\n\nResearch staff review the ranked list, and any outreach action the workflow takes requires\n**human in the loop approval**, so no patient is approached without a person deciding. **PII masking**\nat the gateway, with custom patterns per bot, covers traffic through the platform; the custom\nfunctions should fetch only the record fields each criterion needs. The **audit trail**\nrecords every run, **test suites** compare suggestions with verified screening decisions, and\n**monitors** run scheduled health checks on the screening agent and alert on failure. EU and UAE data residency keeps patient data in region.",[168,171,174],{"question":169,"answer":170},"Does AI trial matching increase enrollment?","The deployments on this page do not report enrollment with and without the tool. Yale Cancer Center's tool screened 98,348 patients across 29 trials since September 2022 and facilitated 117 enrollments, and Cleveland Clinic found 22 eligible patients for a rare disease trial in one week, where the usual process had prescreened nine in a year. Mount Sinai deployed matching systemwide in 2026 and has promised published results; until comparisons are published, treat an enrollment increase as something to measure, not to assume.",{"question":172,"answer":173},"How accurate is it?","Good enough to prioritize, not to decide. On one trial, Cleveland Clinic's research staff confirmed all 22 patients the system identified as eligible (100% positive predictive value), but missed eligible patients are not reported on the Cleveland Clinic page. On its validation trial Yale Cancer Center reports 94% retrospective and 88% prospective accuracy with 100% sensitivity. Both keep staff verification in the loop, so measure missed eligible patients as well as false matches.",{"question":175,"answer":176},"What should we watch out for?","Unequal identification across sites and groups, criteria that fall out of date after protocol amendments, and evaluations run by organizations with a financial interest in the vendor, as Cleveland Clinic discloses for its investment in Dyania Health.",[178],"clinical-and-regulatory-document-drafting","2026-09-27",[181],{"date":179,"note":182},"First published","clinical-trial-patient-matching",[185,213,247],{"title":186,"useCases":187,"organization":188,"vendors":193,"summary":197,"stage":198,"year":36,"channels":199,"languages":200,"metrics":202,"outcomeDisclosed":190,"sources":203,"verification":208,"grade":210,"id":211,"organizationSlug":212},"Mount Sinai: systemwide AI clinical trial matching for cancer patients",[183],{"name":189,"anonymized":190,"country":191,"region":192,"industry":17},"Mount Sinai Health System",false,"US","north-america",[194],{"name":195,"role":196},"Triomics","platform","The Mount Sinai Tisch Cancer Center deployed PRISM, an oncology specific trial matching platform from Triomics built on its OncoLLM language model pipeline, across the Mount Sinai Health System in January 2026. The platform reviews patient records against trial protocols so that patients seen at other hospitals in the system, such as Mount Sinai Queens and Mount Sinai Brooklyn, have the same access to trials as those treated at The Mount Sinai Hospital. Mount Sinai says the aim is also to let clinicians focus on conversations with patients rather than manual chart review; the January 2026 release reports no outcome figures. Mount Sinai said it would evaluate outcomes and publish them later.","production",[26],[201],"en",[],[204],{"url":205,"title":206,"publisher":189,"date":207},"https://www.mountsinai.org/about/newsroom/2026/mount-sinai-launches-ai-powered-clinical-trial-matching-platform-to-expand-access-to-cancer-research","Mount Sinai Launches AI-Powered Clinical Trial-Matching Platform to Expand Access to Cancer Research","2026-01-08",{"level":209,"checkedAt":179},"source-verified","B","mount-sinai-oncology-trial-matching",null,{"title":214,"useCases":215,"organization":216,"vendors":218,"summary":221,"stage":222,"year":223,"channels":224,"languages":225,"metrics":226,"outcomeDisclosed":240,"sources":241,"verification":245,"grade":210,"id":246,"organizationSlug":212},"Cleveland Clinic: language model prescreening for a polycythemia vera trial",[183],{"name":217,"anonymized":190,"country":191,"region":192,"industry":17},"Cleveland Clinic",[219],{"name":220,"role":196},"Dyania Health","Cleveland Clinic researchers used Dyania Health's Synapsis platform, a medically trained language model system embedded in the electronic medical record, to prescreen patients for a phase 3 polycythemia vera trial. From 4.7 million active records it identified 28,200 patients with an oncology diagnosis in the past three years, narrowed them to 904 patients with polycythemia vera, assessed each against the trial's seven eligibility and 20 exclusion criteria within one week and found 22 eligible patients, all confirmed by research staff (100% positive predictive value). The usual workflow had prescreened nine patients and enrolled four over twelve months. Looking ahead, Cleveland Clinic and Dyania Health have announced a collaboration to integrate the platform across the health system's clinical research enterprise; Cleveland Clinic has also invested in Dyania Health.","pilot",2025,[26],[201],[227,235],{"kpi":44,"value":228,"unit":229,"qualifier":230,"period":231,"claimant":232,"quote":233,"sourceUrl":234},904,"count","exact","patients assessed against the trial criteria in one week","organization","“The AI tool completed full eligibility assessments on these 904 patients within one week, against the trial’s criteria and identified 22 eligible patients,” Dr. Gerds and his colleagues reported.","https://consultqd.clevelandclinic.org/ai-screening-platform-accelerates-trial-recruitment-in-polycythemia-vera",{"kpi":45,"value":236,"unit":237,"qualifier":230,"period":238,"claimant":232,"quote":239,"sourceUrl":234},100,"percent","of the 22 patients identified as eligible, confirmed by research staff (positive predictive value)","In a study presented at the 2025 American Society of Hematology (ASH) Annual Meeting, investigators reported that the Dyania Health’s Synapsis™ AI platform, an artificial intelligence tool, identified seven times more eligible patients for a polycythemia vera trial than standard workflows, while achieving 100% positive predictive value following research-staff verification.",true,[242],{"url":234,"title":243,"publisher":244},"AI Screening Platform Accelerates Trial Recruitment in Polycythemia Vera","Cleveland Clinic Consult QD",{"level":209,"checkedAt":179},"cleveland-clinic-ai-trial-prescreening",{"title":248,"useCases":249,"organization":250,"vendors":252,"summary":255,"stage":198,"year":256,"channels":257,"languages":258,"metrics":259,"outcomeDisclosed":240,"sources":268,"verification":276,"grade":210,"id":277,"organizationSlug":212},"Yale Cancer Center: semiautomated clinical trial patient matching on OMOP data",[183],{"name":251,"anonymized":190,"country":191,"region":192,"industry":17},"Yale Cancer Center",[253],{"name":251,"role":254},"in-house","Yale Cancer Center built a clinical trial patient matching (CTPM) tool that combines rules with natural language processing over structured and unstructured record data standardized to the OMOP common data model. Validated first on one metastatic colorectal cancer trial, it was then implemented across 29 trials in several cancer specialties. Since September 2022 it has screened 98,348 patients, identified 825 eligible candidates and contributed to 117 enrollments, and it cut screening time per reviewed chart by 41%. The prescreening is semiautomated: research teams review the candidates the tool puts forward instead of reading every chart.",2022,[26],[201],[260,264],{"kpi":43,"value":261,"unit":237,"qualifier":230,"period":262,"claimant":232,"quote":263,"sourceUrl":35},41,"screening time per chart for patients who underwent review","Implementation reduced chart review workload 10-fold and screening time by 41% (3.1 to 1.8 minutes per chart) for those patients who did undergo review.",{"kpi":44,"value":265,"unit":229,"qualifier":230,"period":266,"claimant":232,"quote":267,"sourceUrl":35},98348,"patients screened since September 2022, across 29 trials","Since September 2022, the system has screened 98,348 patients across 29 trials, identifying 825 eligible candidates and facilitating 117 patient enrollments with 9%-37% consent rates.",[269,273],{"url":35,"title":270,"publisher":271,"date":272},"Clinical Trial Patient Matching: A Real-Time, Common Data Model and Artificial Intelligence-Driven System for Semiautomated Patient Prescreening in Cancer Clinical Trials (abstract record)","Europe PMC","2026-01-09",{"url":274,"title":34,"publisher":275},"https://ascopubs.org/doi/10.1200/CCI-25-00262","JCO Clinical Cancer Informatics",{"level":209,"checkedAt":179},"yale-cancer-center-clinical-trial-patient-matching",0,[280,288,294],{"kpi":44,"label":281,"unit":229,"aggregate":190,"higherIsBetter":240,"n":282,"nUpTo":278,"median":283,"min":228,"max":265,"byClaimant":284,"vendorOnly":190,"points":285},"Interactions handled",2,49626,{"organization":282,"vendor":278,"regulator":278,"independent":278},[286,287],{"evidenceId":277,"organization":251,"value":265,"qualifier":230,"claimant":232,"grade":210,"pooled":240},{"evidenceId":246,"organization":217,"value":228,"qualifier":230,"claimant":232,"grade":210,"pooled":240},{"kpi":45,"label":289,"unit":237,"aggregate":240,"higherIsBetter":240,"n":290,"nUpTo":278,"median":236,"min":236,"max":236,"byClaimant":291,"vendorOnly":190,"points":292},"Accuracy",1,{"organization":290,"vendor":278,"regulator":278,"independent":278},[293],{"evidenceId":246,"organization":217,"value":236,"qualifier":230,"claimant":232,"grade":210,"pooled":240},{"kpi":43,"label":295,"unit":237,"aggregate":240,"higherIsBetter":240,"n":290,"nUpTo":278,"median":261,"min":261,"max":261,"byClaimant":296,"vendorOnly":190,"points":297},"Handling time reduction",{"organization":290,"vendor":278,"regulator":278,"independent":278},[298],{"evidenceId":277,"organization":251,"value":261,"qualifier":230,"claimant":232,"grade":210,"pooled":240},{"low":300,"high":301},12000,210000,[303,322,341,363],{"slug":178,"title":304,"shortTitle":305,"definition":306,"status":9,"industries":307,"functions":308,"patterns":310,"audience":27,"autonomy":313,"adoptionStage":29,"evidenceCount":282,"publicEvidenceCount":282,"organizations":314,"bestGrade":210,"headline":317,"lastVerified":179,"indexable":240},"AI drafting of clinical study reports and regulatory documents","Clinical and regulatory document drafting","Generative AI that drafts clinical study reports and other regulated documents, such as protocols, patient materials and submission modules, from the trial's statistical tables, listings and figures and from approved template text, for medical writers to verify, edit and approve before anything is submitted to a regulator.",[18],[309,20],"regulatory-compliance",[311,312,23],"content-generation","rag-knowledge-assistant","copilot",[315,316],"Merck & Co.","Novo Nordisk",{"kpi":318,"label":319,"unit":237,"n":290,"nUpTo":278,"kind":320,"value":321,"qualifier":230,"claimant":232,"organization":315,"vendorReported":190},"error-reduction","Error reduction","reported",50,{"slug":323,"title":324,"shortTitle":325,"definition":326,"status":9,"industries":327,"functions":328,"patterns":329,"audience":27,"autonomy":28,"adoptionStage":29,"segment":332,"evidenceCount":282,"publicEvidenceCount":282,"organizations":333,"bestGrade":210,"headline":336,"lastVerified":340,"indexable":240},"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],[20,21],[330,24,331],"prediction-and-scoring","anomaly-detection","hospital operations",[334,335],"Humber River Health","Johns Hopkins Medicine",{"kpi":337,"label":338,"unit":237,"n":282,"nUpTo":278,"kind":320,"value":339,"qualifier":230,"claimant":232,"organization":335,"vendorReported":190},"processing-time-reduction","Cycle time reduction",38,"2026-09-28",{"slug":342,"title":343,"shortTitle":344,"definition":345,"status":9,"industries":346,"functions":348,"patterns":351,"audience":27,"autonomy":313,"adoptionStage":29,"segment":349,"evidenceCount":353,"publicEvidenceCount":353,"organizations":354,"bestGrade":210,"headline":360,"lastVerified":179,"indexable":240},"health-prior-authorization-and-claims-adjudication","AI for health insurance prior authorization and claims adjudication support","Health prior authorization and adjudication","AI that reads prior authorization requests, medical claims and appeals with their clinical and billing documents, extracts diagnoses, treatments and costs, checks them against the policy and published clinical criteria, and prepares a summary and recommendation for a clinician or adjudicator, who makes every adverse decision.",[347,17],"insurance",[349,350,20],"claims","case-management",[23,352,312,24,311],"summarization",5,[355,356,357,358,359],"Acentra Health","AdvanceCare","Centers for Medicare and Medicaid Services","ICICI Lombard","Manulife",{"kpi":43,"label":295,"unit":237,"n":282,"nUpTo":278,"kind":320,"value":321,"qualifier":361,"claimant":362,"organization":355,"vendorReported":240},"approximately","vendor",{"slug":364,"title":365,"shortTitle":366,"definition":367,"status":9,"industries":368,"functions":370,"patterns":371,"audience":373,"autonomy":374,"adoptionStage":29,"evidenceCount":375,"publicEvidenceCount":375,"organizations":376,"bestGrade":210,"headline":212,"lastVerified":179,"indexable":240},"adverse-event-case-intake","AI for pharmacovigilance adverse event case intake","Adverse event case intake","AI that takes in adverse event reports about medicines, vaccines and devices from calls, emails, forms, literature and partner files, decides whether each is a valid case, flags seriousness, extracts and codes the case data into the safety database format, and routes it to drug safety professionals, who review medical content and regulatory reporting.",[18,369],"government",[309,350,20],[23,24,372],"conversational-agent","back-office","supervised-agent",4,[377,378,379,380],"Bayer","U.S. Food and Drug Administration, Center for Drug Evaluation and Research","U.S. Food and Drug Administration","Pfizer",{"indexable":240,"reasons":382},[],[384,389,394,402,408,414,421,428,436,443,450,456,463,469,475,480,487,493,498,504,510,516,522,527,532,539,546,551,557,564,570,576,582,587],{"id":143,"label":385,"issuer":156,"region":151,"url":386,"description":387,"useCases":388,"indexable":240},"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":144,"label":390,"issuer":156,"region":151,"url":391,"description":392,"useCases":393,"indexable":240},"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":395,"label":396,"issuer":397,"region":398,"url":399,"description":400,"useCases":401,"indexable":240},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":146,"label":403,"issuer":404,"region":192,"url":405,"description":406,"useCases":407,"indexable":240},"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":409,"label":410,"issuer":156,"region":151,"url":411,"description":412,"useCases":413,"indexable":240},"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":415,"label":416,"issuer":417,"region":151,"url":418,"description":419,"useCases":420,"indexable":240},"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":422,"label":423,"issuer":424,"region":151,"url":425,"description":426,"useCases":427,"indexable":240},"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":429,"label":430,"issuer":431,"region":432,"url":433,"description":434,"useCases":435,"indexable":240},"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":437,"label":438,"issuer":439,"region":432,"url":440,"description":441,"useCases":442,"indexable":240},"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":444,"label":445,"issuer":446,"region":398,"url":447,"description":448,"useCases":449,"indexable":240},"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":451,"label":452,"issuer":453,"region":192,"url":454,"description":455,"useCases":449,"indexable":240},"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":457,"label":458,"issuer":459,"region":151,"url":460,"description":461,"useCases":462,"indexable":240},"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":464,"label":465,"issuer":466,"region":398,"url":467,"description":468,"useCases":60,"indexable":240},"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":470,"label":471,"issuer":156,"region":151,"url":472,"description":473,"useCases":474,"indexable":240},"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":476,"label":477,"issuer":156,"region":151,"url":478,"description":479,"useCases":474,"indexable":240},"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":481,"label":482,"issuer":483,"region":192,"url":484,"description":485,"useCases":486,"indexable":240},"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":488,"label":489,"issuer":156,"region":151,"url":490,"description":491,"useCases":492,"indexable":240},"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":145,"label":494,"issuer":495,"region":192,"url":496,"description":497,"useCases":492,"indexable":240},"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":499,"label":500,"issuer":501,"region":398,"url":502,"description":503,"useCases":492,"indexable":240},"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":505,"label":506,"issuer":156,"region":151,"url":507,"description":508,"useCases":509,"indexable":240},"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":511,"label":512,"issuer":513,"region":192,"url":514,"description":515,"useCases":509,"indexable":240},"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":517,"label":518,"issuer":431,"region":432,"url":519,"description":520,"useCases":521,"indexable":240},"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":523,"label":524,"issuer":156,"region":151,"url":525,"description":526,"useCases":521,"indexable":240},"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":528,"label":529,"issuer":156,"region":151,"url":530,"description":531,"useCases":521,"indexable":240},"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":533,"label":534,"issuer":535,"region":151,"url":536,"description":537,"useCases":538,"indexable":240},"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":540,"label":541,"issuer":542,"region":192,"url":543,"description":544,"useCases":545,"indexable":240},"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":547,"label":548,"issuer":156,"region":151,"url":549,"description":550,"useCases":545,"indexable":240},"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":552,"label":553,"issuer":156,"region":151,"url":554,"description":555,"useCases":556,"indexable":240},"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.",6,{"id":558,"label":559,"issuer":560,"region":561,"url":562,"description":563,"useCases":353,"indexable":240},"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":565,"label":566,"issuer":567,"region":151,"url":568,"description":569,"useCases":375,"indexable":240},"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.",{"id":571,"label":572,"issuer":573,"region":151,"url":574,"description":575,"useCases":375,"indexable":240},"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":577,"label":578,"issuer":579,"region":432,"url":580,"description":581,"useCases":59,"indexable":240},"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":583,"label":584,"issuer":156,"region":151,"url":585,"description":586,"useCases":59,"indexable":240},"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":588,"label":589,"issuer":590,"region":192,"url":591,"description":592,"useCases":59,"indexable":240},"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.",1790598297695]