[{"data":1,"prerenderedAt":101},["ShallowReactive",2],{"uc-org-sanofi":3},{"organization":4,"includeUnpublished":13,"evidence":14},{"slug":5,"name":6,"country":7,"region":8,"industry":9,"wikidata":10,"records":11,"useCases":11,"indexable":12},"sanofi","Sanofi","FR","europe","pharma-and-life-sciences","Q158205",3,true,false,[15,44,75],{"title":16,"useCases":17,"organization":19,"vendors":20,"summary":23,"stage":24,"year":25,"channels":26,"languages":28,"metrics":30,"outcomeDisclosed":13,"sources":31,"verification":36,"grade":39,"id":40,"useCaseTitles":41},"Sanofi: AI for vaccine trial recruitment feasibility and site planning",[18],"clinical-trial-site-selection-and-feasibility",{"name":6,"anonymized":13,"country":7,"region":8,"industry":9,"wikidata":10},[21],{"name":6,"role":22},"in-house","Sanofi describes using AI in its clinical development organization to analyze historical trial data and disease patterns so study teams can estimate realistic timelines and predict the number of eligible participants at different locations before choosing where to run a trial. Christian Mornet, Sanofi's Clinical Digital Lead for Vaccines, says the approach spots patterns across prior studies \"from any origin\" to improve how trials are designed and targeted, aiming for more efficient and reliable recruitment than making educated guesses.","production",2025,[27],"internal-tools",[29],"en",[],[32],{"url":33,"title":34,"publisher":6,"date":35},"https:\u002F\u002Fwww.sanofi.com\u002Fen\u002Fmagazine\u002Fai-in-healthcare\u002Fai-across-the-randd-value-chain-clinical-development","AI Across the R&D Value Chain: Clinical Development","2025-09-04",{"level":37,"checkedAt":38},"source-verified","2026-09-29","B","sanofi-ai-trial-recruitment-feasibility",[42],{"slug":18,"title":43},"AI for clinical trial site selection and feasibility",{"title":45,"useCases":46,"organization":48,"vendors":49,"summary":51,"stage":24,"year":52,"channels":53,"languages":54,"metrics":55,"outcomeDisclosed":12,"sources":65,"verification":69,"grade":39,"id":71,"useCaseTitles":72},"Sanofi: AI and machine learning to close minor manufacturing deviations faster",[47],"batch-record-and-deviation-review",{"name":6,"anonymized":13,"country":7,"region":8,"industry":9,"wikidata":10},[50],{"name":6,"role":22},"Sanofi's manufacturing and supply organization gives site teams AI and machine learning tools that search for similar deviations at other sites and in the past, and that automate the review and trending of minor deviations, the largest category of deviations. The executive vice president for manufacturing and supply reports that this cut deviation closure time. The same article describes SimplY, an AI powered yield analytics platform that flags production outliers, but states no number for it.",2023,[27],[],[56],{"kpi":57,"value":58,"unit":59,"qualifier":60,"period":61,"claimant":62,"quote":63,"sourceUrl":64},"processing-time-reduction",60,"percent","exact","deviation closure time, as reported in June 2023","organization","By equipping Sanofians with AI & ML capabilities that allow us to search for and learn from similar deviations at other sites, or in the past while automating the review and trending of minor deviations, we’ve managed to reduce closure time by 60%, lowering cycle times and boosting productivity while strengthening quality and supply reliability.","https:\u002F\u002Fwww.sanofi.com\u002Fen\u002Fmagazine\u002Four-science\u002Fai-goes-industrial",[66],{"url":64,"title":67,"publisher":6,"date":68},"AI Goes Industrial","2023-06-19",{"level":37,"checkedAt":70},"2026-11-12","sanofi-ai-deviation-closure-and-yield-analytics",[73],{"slug":47,"title":74},"AI review of batch records and process data for quality deviations in pharma manufacturing",{"title":76,"useCases":77,"organization":79,"vendors":80,"summary":84,"stage":24,"year":85,"channels":86,"languages":87,"metrics":88,"outcomeDisclosed":12,"sources":89,"verification":95,"grade":96,"id":97,"useCaseTitles":98},"Sanofi: automated case intake on Deloitte's ConvergeHEALTH Safety platform",[78],"adverse-event-case-intake",{"name":6,"anonymized":13,"country":7,"region":8,"industry":9,"wikidata":10},[81],{"name":82,"role":83},"Deloitte","integrator","Sanofi and Deloitte spent more than two years building an AI, software as a service adverse event case intake platform, ConvergeHEALTH Safety, that automates the capture, entry and coding of safety information before it is reported to regulators worldwide. Stephen Kuske, Sanofi's global head of pharmacovigilance operations, said the collaboration let Sanofi optimize quality and efficiency at the same time for the first time, and Sanofi's head of digital pharmacovigilance, Anand Ramanathan, said the initiative frees pharmacovigilance staff to focus on patient safety. Deloitte reports that the initial deployment already improved case quality and case processing efficiency.",2021,[27],[29],[],[90],{"url":91,"title":92,"publisher":93,"date":94},"https:\u002F\u002Fwww.prnewswire.com\u002Fnews-releases\u002Fdeloitte-and-sanofi-automate-pharmacovigilance-case-processing-301424029.html","Deloitte and Sanofi Automate Pharmacovigilance Case Processing","PR Newswire (Deloitte)","2021-11-15",{"level":37,"checkedAt":38},"C","sanofi-deloitte-convergehealth-safety-intake",[99],{"slug":78,"title":100},"AI for pharmacovigilance adverse event case intake",1794572868472]