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artificielle",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"},{"id":"breadcrumb-ec907b9121-item-0035fefd38","link":{"valid":true,"url":"/fr/artificial-intelligence/industries/"},"active":false,"current":false,"title":"L’IA par secteur d’activité",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"},{"id":"breadcrumb-ec907b9121-item-1cb194958a","link":{"valid":true,"url":"/fr/artificial-intelligence/industries/ai-in-healthcare/"},"active":true,"current":true,"title":"L’IA dans la santé",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_939100716":{"id":"flexible-column-container-282ffde577","propertiesId":"hub-hero","type":"2-column-even","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-4065acfc42",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2":{"id":"title-v2-8afef68b84","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Mise à l’échelle de l’IA dans la santé : cas d’usage, socles data et gouvernance"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-8c21abf814","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EDécouvrez comment les organismes de santé appliquent l’IA aux soins cliniques, aux opérations, à l’accès et à la recherche, et ce qu’il faut pour mettre à l’échelle ces initiatives de manière responsable.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-b8c1306c2f",":type":"snowflake-site/components/container",":items":{"content_chip":{"id":"content-chip-1307949ba4","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?quality=85&preferwebp=true","alt":"Laurie MacPherson","lazyEnabled":true,"isLcpImage":true,"width":"800",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Laurie MacPherson","Technical Writer, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip_copy":{"id":"content-chip-f1b3846694","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"709","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--9ad7a3c0-e23e-4370-bc36-f59a5fe02eff/img-1817.jpg?quality=85&preferwebp=true","alt":"Tracy Kabuya","lazyEnabled":true,"isLcpImage":false,"width":"709",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Tracy Kabuya","Contributrice locale, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip","content_chip_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["title_v2","text","container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-f1f6cbc436",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-6600334253","youtubeVideoId":"CBaLM0qkIr0","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-5e53db24d5","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container border-top","layout":"SIMPLE","id":"container-98619d39f3",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-93c30e8d22","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003ESujets liés à l’IA par secteur :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-7dbcd593c3","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-advertising\"\u003EL’IA dans la publicité\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/industries/ai-in-cybersecurity/\"\u003EL’IA dans la cybersécurité\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-finance/\"\u003EL’IA dans les services financiers\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-manufacturing/\"\u003EL’IA dans le secteur de l’industrie\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-retail/\"\u003EL’IA dans le retail\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-8bad7a53c3","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text__0":{"id":"text-f67c50f50e","text":"\u003Cp\u003ELes organismes de santé doivent aujourd’hui faire face à des marges plus étroites, à l’évolution des modèles de remboursement, à une pression accrue sur le personnel et à des attentes croissantes en matière d’accès, de résultats et d’efficacité opérationnelle.\u003C/p\u003E\n\u003Cp\u003EDans ce contexte, les équipes agissent rapidement pour mettre en œuvre de nouvelles formes d'IA.  Dans \u003Ca href=\"https://www.snowflake.com/en/future-ai-interoperability-healthcare/\"\u003El’enquête 2026 de Snowflake et Hakkoda\u003C/a\u003E menée auprès de 183 hauts dirigeants américains du secteur de la santé, 77 % ont déclaré que leur organisation avait investi ou prévoyait d’investir dans \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/generative-ai/\"\u003El’IA générative\u003C/a\u003E ou \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/agents/\"\u003El’IA agentique\u003C/a\u003E, avec la documentation clinique, l’automatisation des workflows administratifs et les opérations du cycle de facturation parmi les priorités. Ces investissements élargissent un éventail de capacités qui comprend déjà le \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E prédictif, le \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/natural-language-processing/\"\u003Etraitement du langage naturel\u003C/a\u003E et la \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/computer-vision/\"\u003Evision par ordinateur\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003ELes cas d’usage dans la santé imposent cependant des exigences particulières à chaque forme d’IA. Les informations protégées des patients sont réparties entre divers systèmes et organisations, la signification clinique peut changer en fonction du moment et du cadre de soins, et un résultat incomplet ou incorrect peut avoir de graves conséquences.\u003C/p\u003E\n\u003Cp\u003EComme \u003Ca href=\"https://www.snowflake.com/en/news/press-releases/snowflake-research-reveals-85-percent-of-healthcare-leaders-view-interoperability-as-foundational-to-scaling-ai/\"\u003El’explique\u003C/a\u003E \u003Ca href=\"https://www.snowflake.com/en/blog/authors/jesse-cugliotta/\"\u003EJesse Cugliotta\u003C/a\u003E, Global Head of Healthcare and Life Sciences chez Snowflake : « Dans l’ensemble du secteur de la santé, l’IA s’intègre aux environnements opérationnels et les dirigeants lui imposent des normes plus strictes. » L’IA dans la santé dépend de la manière dont les données, les modèles, les individus et les contrôles interagissent.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-ai-supports-clinical-care":{"id":"title-v2-4706c0e711","additionalClasses":"anchor-title anchor-title--how-ai-supports-clinical-care","type":"heading2","lines":["Comment l’IA soutient les soins cliniques"],":type":"snowflake-site/components/title-v2"},"text_how-ai-supports-clinical-care_0":{"id":"text-5d88ff0013","text":"\u003Cp\u003EL’IA clinique peut aider les équipes soignantes à interpréter de grands volumes d’informations sur les patients, à identifier les schémas qui méritent une attention particulière et à réduire le travail nécessaire pour rassembler le contexte pertinent. Le niveau d’implication humaine reste élevé, en particulier lorsqu’un résultat pourrait influencer le diagnostic ou le traitement.\u003C/p\u003E\n\u003Ch3\u003EImagerie médicale et aide au diagnostic\u003C/h3\u003E\n\u003Cp\u003ELes modèles de vision par ordinateur peuvent contribuer à l'analyse des radiographies, des scanners, des examens IRM, des images échographiques et des lames d'anatomopathologie numérique.  Selon l’application, l’IA peut signaler une anomalie suspectée, délimiter une zone d’intérêt, comparer des images ou aider à prioriser les examens pouvant nécessiter une analyse urgente.\u003C/p\u003E\n\u003Cp\u003ECes outils fonctionnent dans le contexte plus large du dossier clinique. Une observation sur une image doit être interprétée en tenant compte des symptômes, des résultats de laboratoire, des examens antérieurs, des traitements en cours et des antécédents médicaux. Les modèles multimodaux commencent à prendre en charge ce type d’analyse élargie, en combinant des images médicales avec du texte et des données cliniques structurées.\u003C/p\u003E\n\u003Cp\u003EL’IA peut également aider à gérer les workloads d’imagerie. En identifiant les examens présentant des anomalies aiguës potentielles, un système peut les faire avancer dans la file d'attente de lecture, tandis que les cas de routine restent disponibles pour interprétation dans leur ordre normal. Le radiologue ou un autre clinicien agréé continue d’interpréter l’examen et de déterminer la signification clinique du résultat.\u003C/p\u003E\n\u003Ch3\u003EAide à la décision clinique et prédiction des risques\u003C/h3\u003E\n\u003Cp\u003ELes modèles prédictifs de machine learning estiment la probabilité d’un événement à l’aide des informations déjà présentes dans le dossier clinique. Les applications courantes incluent l’identification des patients présentant un risque de détérioration, de réadmission, de septicémie, d’effets indésirables liés aux médicaments ou de progression de la maladie.\u003C/p\u003E\n\u003Cp\u003ELe score en lui-même n’est qu’un indicateur parmi d’autres. Un patient classé à haut risque peut nécessiter une surveillance plus étroite, un examen clinique ou des tests supplémentaires, selon son état et le cadre de soins. Pour appuyer cette décision, le système peut avoir besoin des observations actuelles, des consultations antérieures, des traitements en cours et du calendrier des interventions récentes, et non d’un simple code de diagnostic ou d’une demande de remboursement passée.\u003C/p\u003E\n\u003Cp\u003ELa calibration est particulièrement importante. Deux patients se voyant attribuer le même score de risque devraient présenter une probabilité similaire d’obtenir le résultat prédit, tandis que la performance doit rester fiable quels que soient les groupes démographiques, les environnements de soins et les sous-populations cliniques. Sans cette calibration, un modèle peut sembler précis dans son ensemble tout en offrant de mauvaises performances pour des patients individuels.\u003C/p\u003E\n\u003Ch3\u003ESoins personnalisés et de précision\u003C/h3\u003E\n\u003Cp\u003EL’IA peut aider les cliniciens à analyser les nombreux facteurs qui influencent la réponse au traitement, notamment les diagnostics, les résultats de laboratoire, les médicaments, les antécédents médicaux, les biomarqueurs et les informations génomiques. En oncologie, par exemple, les modèles peuvent faciliter la stratification des traitements en identifiant des tendances à travers les caractéristiques des tumeurs et les résultats antérieurs. Pour les maladies chroniques, l’IA peut aider à mettre en évidence les changements de risque ou d’observance qui suggèrent qu’un plan de soins doit être révisé.\u003C/p\u003E\n\u003Cp\u003ELa personnalisation repose également sur un dossier longitudinal. Les informations issues d’une consultation isolée reflètent rarement l’évolution complète d’une pathologie, en particulier lorsqu’un patient reçoit des soins de plusieurs établissements. La connexion des données cliniques, pharmaceutiques, de facturation et générées par les patients offre aux modèles une vision plus complète des traitements antérieurs et de leurs résultats.\u003C/p\u003E\n\u003Cp\u003ELa recommandation qui en résulte nécessite toujours un jugement clinique. Les préférences, les contre-indications et la situation du patient peuvent ne pas être entièrement représentées dans les données, même lorsque le modèle a accès à des antécédents détaillés.\u003C/p\u003E\n\u003Ch3\u003EDocumentation clinique et IA ambiante\u003C/h3\u003E\n\u003Cp\u003ELa documentation clinique consomme un temps que les cliniciens pourraient autrement consacrer aux patients ou à d’autres responsabilités de soins. Les systèmes de documentation ambiante utilisent la reconnaissance vocale, le traitement du langage naturel et l’IA générative pour capturer la conversation entre le clinicien et le patient et préparer un brouillon de note.\u003C/p\u003E\n\u003Cp\u003EUn système bien conçu peut organiser la consultation en sections pertinentes, extraire les médicaments ou les actions de suivi et relier le brouillon aux informations déjà présentes dans le dossier de santé électronique (DSE). Le clinicien examine, modifie et approuve ensuite la note avant qu’elle ne soit intégrée au dossier médical.\u003C/p\u003E\n\u003Cp\u003EL’IA générative peut prendre en charge des tâches de documentation connexes, notamment résumer de longs antécédents médicaux, préparer un résumé de sortie ou rédiger une transmission. Dans chaque cas, le système doit distinguer les informations sources du langage généré et permettre aux cliniciens de vérifier facilement le résultat.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_how-ai-supports-clinical-care_0":{"id":"card-v2-c637163cf1","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["TÉMOIGNAGE CLIENT : Bupa"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire l’étude de cas complète"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c35f80d5-4aef-4888-8d73-84dc1e903a75/bupa.png?quality=85&preferwebp=true","alt":"Bupa logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EBupa utilise l’AI Data Cloud de Snowflake pour connecter les données des équipes de soins de santé, d’assurance, de soins dentaires, de services de soins et de services de santé. Avec Snowflake au cœur de son écosystème de données fédéré, Bupa a réduit le temps d’ingestion des données de son centre de contact de 90 %, a atteint une disponibilité de 99,9 % pour les données de ce centre et explore des cas d’usage de l’IA tels que la détection des fraudes, les assistants virtuels, les informations cliniques et l’orientation personnalisée des soins.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_how-ai-supports-patient-access-and-care-management":{"id":"title-v2-6b189a3111","additionalClasses":"anchor-title anchor-title--how-ai-supports-patient-access-and-care-management","type":"heading2","lines":["Comment l’IA favorise l’accès des patients et la gestion des soins"],":type":"snowflake-site/components/title-v2"},"text_how-ai-supports-patient-access-and-care-management_0":{"id":"text-2d85e81e8b","text":"\u003Cp\u003EDe nombreux obstacles aux soins apparaissent avant ou entre les consultations cliniques. Les patients peuvent avoir des difficultés à trouver le bon prestataire, à prendre rendez-vous, à comprendre leur couverture ou à suivre un plan de soins, tandis que le personnel coordonne les orientations et les interventions à travers plusieurs systèmes déconnectés.\u003C/p\u003E\n\u003Ch3\u003EAccès et orientation des patients\u003C/h3\u003E\n\u003Cp\u003EL’IA peut faciliter la prise de rendez-vous, la mise en relation avec les prestataires, l’orientation des patients, les questions d’éligibilité et la gestion des listes d’attente. Un patient cherchant à prendre rendez-vous, par exemple, peut avoir besoin d’un prestataire ayant la bonne spécialité, le bon statut de réseau, le bon emplacement et la bonne disponibilité. Un système d’IA peut rassembler ces contraintes et restreindre les options disponibles.\u003C/p\u003E\n\u003Cp\u003EL’IA agentique peut coordonner une plus grande partie du workflow. Avec un accès aux outils approuvés, un agent pourrait examiner une orientation, identifier les informations manquantes, vérifier les garanties et trouver un rendez-vous qui répond aux besoins du patient. Les étapes comportant une urgence clinique, des règles de prise en charge atypiques ou des exceptions peuvent être acheminées vers un collaborateur. \u003C/p\u003E\n\u003Ch3\u003EGestion des soins et santé de la population\u003C/h3\u003E\n\u003Cp\u003ELes équipes de gestion des soins travaillent sur des panels de patients, identifiant ceux qui pourraient avoir besoin d’une intervention et coordonnant les services entre les prestataires et les établissements. Les modèles prédictifs peuvent aider à identifier les patients à risque croissant, les lacunes en matière de soins préventifs, les suivis manqués ou les tendances associées à une utilisation évitable des services.\u003C/p\u003E\n\u003Cp\u003EÀ l’échelle de la population, l’IA peut aider les organisations à stratifier les risques, à évaluer les résultats des programmes et à identifier les disparités entre les groupes de patients. Ces analyses peuvent intégrer les dossiers cliniques, les demandes de remboursement, les données pharmaceutiques et les déterminants sociaux de la santé.\u003C/p\u003E\n\u003Cp\u003ELes définitions sous-jacentes au modèle déterminent qui apparaît dans chaque groupe. Une mesure des lacunes dans les soins, par exemple, dépend de la population éligible, des critères d’exclusion, de la période de mesure et des preuves jugées suffisantes pour combler cet écart. Un contexte clinique et sémantique partagé permet de garder ces règles homogènes lorsqu’elles sont utilisées dans des analyses, des interfaces conversationnelles ou des workflows d’agents.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-ai-supports-healthcare-operations-and-administration":{"id":"title-v2-0fcfd1483e","additionalClasses":"anchor-title anchor-title--how-ai-supports-healthcare-operations-and-administration","type":"heading2","lines":["Comment l’IA soutient les opérations et l’administration des soins de santé"],":type":"snowflake-site/components/title-v2"},"text_how-ai-supports-healthcare-operations-and-administration_0":{"id":"text-dc4fe52794","text":"\u003Cp\u003ELe travail administratif façonne le coût, la capacité et l’accessibilité des soins. Une grande partie de ce travail consiste à recueillir des informations, à appliquer des règles complexes et à coordonner les transferts entre les systèmes, des conditions qui font des opérations de santé un domaine de prédilection pour l’IA générative et agentique.\u003C/p\u003E\n\u003Ch3\u003ECapacité, effectifs et flux de patients\u003C/h3\u003E\n\u003Cp\u003ELes hôpitaux et les systèmes de santé peuvent utiliser des modèles prédictifs pour prévoir l'activité des urgences, l'occupation des lits, le volume de sorties et les rendez-vous non honorés. Ces estimations facilitent les décisions concernant les effectifs, la capacité en lits, les plannings des salles d’opération et d’autres ressources limitées.\u003C/p\u003E\n\u003Cp\u003ELe flux de patients dépend des événements qui se produisent tout au long du parcours de soins. Un examen retardé peut repousser la sortie, et la contrainte de lits qui en résulte peut affecter l’attente aux urgences et les admissions programmées. L’IA peut aider les équipes à repérer ces dépendances plus tôt en reliant l’activité clinique actuelle aux données opérationnelles.\u003C/p\u003E\n\u003Cp\u003EPour la planification des effectifs, les modèles peuvent estimer le workload et les besoins de couverture par unité ou par période afin d’aligner les compétences et la capacité appropriées sur les besoins attendus des patients.\u003C/p\u003E\n\u003Ch3\u003EGestion du cycle de facturation\u003C/h3\u003E\n\u003Cp\u003ELes équipes chargées du cycle de facturation traduisent les soins cliniques en demandes de remboursement précises et gèrent les tâches nécessaires à la réception des paiements. L’IA peut faciliter le codage clinique, la saisie des frais, la préparation des demandes de remboursement, la prédiction des refus et la priorisation des comptes clients.\u003C/p\u003E\n\u003Cp\u003ELe traitement du langage naturel peut extraire les diagnostics et les procédures pertinents de la documentation clinique, tandis que l’IA générative peut résumer les raisons du refus d’une demande et récupérer la politique de l’organisme payeur associée à cette décision. Un agent peut rassembler le dossier justificatif, identifier la documentation manquante et préparer un recours pour examen.\u003C/p\u003E\n\u003Cp\u003ELe dossier source doit rester la référence. Le codage ou le texte de recours généré doit toujours être vérifié par rapport à la documentation clinique et aux règles de facturation applicables, en particulier lorsque des erreurs pourraient compromettre le remboursement, la conformité ou la part à charge du patient.\u003C/p\u003E\n\u003Ch3\u003EAutorisation préalable et gestion de l’utilisation\u003C/h3\u003E\n\u003Cp\u003EL’autorisation préalable exige des prestataires et des organismes payeurs qu’ils échangent des informations cliniques et évaluent si un service demandé répond aux critères de couverture. Ce processus s’étend souvent aux dossiers de santé électroniques (DSE), aux portails des organismes payeurs, aux référentiels de documents et aux outils de communication.\u003C/p\u003E\n\u003Cp\u003EL’IA peut extraire les détails requis du dossier, les comparer aux critères publiés et identifier les informations manquantes avant l’envoi d’une soumission. Un agent pourrait ensuite préparer la demande, suivre son statut et transmettre une réponse au collaborateur approprié.\u003C/p\u003E\n\u003Cp\u003ELes décisions cliniques et de couverture nécessitent des limites d’autorité claires. L’IA peut aider à rassembler des preuves ou à appliquer des règles simples, tandis que les cas impliquant un jugement clinique, des critères ambigus ou des décisions défavorables restent soumis à un examen humain qualifié.\u003C/p\u003E\n\u003Ch3\u003ETraitement des demandes de remboursement et intégrité des paiements\u003C/h3\u003E\n\u003Cp\u003EPour les organismes payeurs du secteur de la santé, l'IA peut aider à évaluer les demandes de remboursement, à détecter les informations incohérentes et à identifier les régularités associées à la fraude, au gaspillage ou aux abus.  Les modèles peuvent signaler une double facturation, une utilisation inhabituelle, des problèmes d’identité ou un comportement du prestataire qui s’écarte d’un schéma attendu.\u003C/p\u003E\n\u003Cp\u003ELes faux positifs peuvent retarder le paiement approprié et créer un travail considérable pour les prestataires et les membres. Les enquêtes doivent donc tenir compte de la fiabilité et des conséquences du signal. L’explicabilité et l’examen sont particulièrement importants lorsqu’un modèle affecte l’accès aux prestations ou au paiement.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-ai-supports-healthcare-research":{"id":"title-v2-de32106c9e","additionalClasses":"anchor-title anchor-title--how-ai-supports-healthcare-research","type":"heading2","lines":["Comment l’IA soutient la recherche en santé"],":type":"snowflake-site/components/title-v2"},"text_how-ai-supports-healthcare-research_0":{"id":"text-1472c7b7d8","text":"\u003Cp\u003ELa recherche en santé s’appuie de plus en plus sur des données en conditions réelles générées par les soins de routine, notamment les DSE, les demandes de remboursement, les registres et les résultats signalés par les patients. L’IA peut aider les chercheurs à identifier des cohortes pertinentes, à analyser les résultats et à trouver des schémas dans des jeux de données difficiles à examiner manuellement.\u003C/p\u003E\n\u003Ch3\u003ERecherche clinique et opérations d’essais\u003C/h3\u003E\n\u003Cp\u003EL’IA peut être utilisée dans la découverte de médicaments, en soutenant la faisabilité des protocoles, la sélection des sites, le recrutement des patients et le suivi des essais. Une équipe de recherche peut estimer si suffisamment de patients éligibles sont disponibles sur les sites participants, puis identifier les dossiers qui semblent répondre aux critères d’inclusion pour un examen plus approfondi.\u003C/p\u003E\n\u003Cp\u003ELe traitement du langage naturel peut extraire des notes et des rapports des informations qui ne sont pas disponibles dans des champs structurés. L’IA générative peut faciliter la revue de la littérature ou la préparation de documents, tandis que des agents peuvent coordonner les étapes administratives approuvées dans les systèmes d’essais.\u003C/p\u003E\n\u003Ch3\u003EPreuves en conditions réelles et pharmacovigilance\u003C/h3\u003E\n\u003Cp\u003ELes études de preuves en conditions réelles examinent les performances des traitements en dehors des essais strictement contrôlés. Les chercheurs peuvent utiliser l’IA pour analyser les résultats dans les dossiers cliniques, les demandes de remboursement et les registres, comparer les cohortes de patients et identifier les variables associées à l’efficacité ou à la sécurité.\u003C/p\u003E\n\u003Cp\u003EEn pharmacovigilance, le traitement du langage naturel et le machine learning peuvent aider à identifier les événements indésirables potentiels dans les récits cliniques, les rapports de sécurité et d’autres sources. Le signal entre ensuite dans un processus d’examen établi : l’IA peut prioriser les preuves, mais ce sont les experts en sécurité qui évaluent la causalité et l’importance réglementaire.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_benefits-and-risks-of-ai-in-healthcare":{"id":"title-v2-8a88f39fd2","additionalClasses":"anchor-title anchor-title--benefits-and-risks-of-ai-in-healthcare","type":"heading2","lines":["Avantages et risques de l’IA dans le secteur de la santé"],":type":"snowflake-site/components/title-v2"},"text_benefits-and-risks-of-ai-in-healthcare_0":{"id":"text-e1fd090414","text":"\u003Cp\u003EL’IA peut aider les organismes de santé à identifier les risques plus tôt, à réduire la charge clinique et administrative, à améliorer l’accès des patients, à utiliser la capacité plus efficacement et à accélérer la recherche. En rassemblant des informations provenant de plusieurs sources, elle peut également favoriser des soins plus coordonnés et personnalisés.\u003C/p\u003E\r\n\u003Cp\u003ECependant, des résultats inexacts, des \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003Ebiais de modèle\u003C/a\u003E, des biais d’automatisation et des variations de performances peuvent affecter les décisions cliniques ou financières. Les organismes de santé doivent également protéger les informations de santé protégées (PHI), \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/ai-compliance/\"\u003Ese conformer aux exigences\u003C/a\u003E telles que la norme \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html\"\u003EHIPAA\u003C/a\u003E et le règlement \u003Ca href=\"https://www.snowflake.com/fr/data-governance/regulatory-compliance/gdpr/\"\u003ERGPD\u003C/a\u003E, tenir compte du consentement et de l’utilisation autorisée, et maintenir une \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/responsible-ai/\"\u003Esupervision humaine appropriée\u003C/a\u003E. Les systèmes hérités et les données incohérentes peuvent ajouter une autre source d’erreur lorsqu’une application reçoit des informations incomplètes ou mal contextualisées.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-healthcare-organizations-need-to-scale-ai":{"id":"title-v2-bac58726ad","additionalClasses":"anchor-title anchor-title--what-healthcare-organizations-need-to-scale-ai","type":"heading2","lines":["Ce dont les organismes de santé ont besoin pour mettre à l’échelle l’IA"],":type":"snowflake-site/components/title-v2"},"text_what-healthcare-organizations-need-to-scale-ai_0":{"id":"text-46059f0da8","text":"\u003Cp\u003EL’extension de l’IA aux tâches cliniques, opérationnelles et administratives nécessite une base plus large : des données patients et stratégiques interopérables ; la prise en charge de modèles prédictifs, génératifs et multimodaux ; un calcul évolutif ; un contexte clinique partagé ; une évaluation continue ; et une gouvernance couvrant les données, les modèles, les outils et les actions.\u003C/p\u003E\r\n\u003Ch3\u003EContexte longitudinal des patients et des membres\u003C/h3\u003E\r\n\u003Cp\u003ELes informations des patients sont réparties entre les DSE, les demandes de remboursement, les laboratoires, les pharmacies, les systèmes d’imagerie, les dispositifs médicaux et d’autres sources. Un dossier longitudinal relie ces événements au fil du temps, offrant aux systèmes d’IA une vision plus claire des diagnostics, des traitements, de l’utilisation et des résultats.\u003C/p\u003E\r\n\u003Cp\u003ELa résolution d’identité est au cœur de ce travail. Les noms, les adresses, les identifiants des membres et les numéros de dossiers médicaux peuvent varier d’un système à l’autre, tandis que des dossiers en double ou mal liés peuvent créer un risque clinique important. La base doit relier les dossiers avec précision sans affaiblir les contrôles de confidentialité qui leur sont appliqués.\u003C/p\u003E\r\n\u003Cp\u003ELa chronologie et la provenance ajoutent un contexte supplémentaire. Un résultat de laboratoire préliminaire diffère d’un résultat final, et un traitement antérieur diffère de celui que le patient prend activement. L’IA doit savoir d’où proviennent les informations, quand elles ont été enregistrées et comment elles doivent être interprétées dans le cadre de leur utilisation actuelle.\u003C/p\u003E\r\n\u003Ch3\u003EInteropérabilité dans le secteur de la santé\u003C/h3\u003E\r\n\u003Cp\u003EL’interopérabilité permet aux informations cliniques, administratives et financières de circuler entre les systèmes sous une forme exploitable par l’organisation réceptrice. Des normes telles que HL7 et \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://ecqi.healthit.gov/fhir/about\"\u003EFast Healthcare Interoperability Resources\u003C/a\u003E (FHIR) fournissent des structures pour l’échange de données de santé, tandis que des modèles communs tels qu’OMOP favorisent une analyse homogène des données d’observation.\u003C/p\u003E\r\n\u003Cp\u003ECependant, l’échange technique ne résout qu’une partie du problème. Les organisations ont également besoin d’une terminologie homogène pour les diagnostics, les procédures, les médicaments, les résultats de laboratoire, les prestataires et les établissements. Sans cet alignement sémantique, deux systèmes peuvent échanger un dossier avec succès tout en attribuant des significations différentes au même champ.\u003C/p\u003E\r\n\u003Cp\u003EPour l’IA, l’interopérabilité élargit le contexte dont dispose un modèle ou un agent. Elle peut relier un événement clinique aux soins antérieurs, aux informations de prise en charge et aux résultats ultérieurs, plutôt que de traiter chaque système comme une vision cloisonnée du patient. C’est pourquoi l’interopérabilité est désormais au cœur de la stratégie d’IA dans le domaine de la santé. Cugliotta explique : « L’interopérabilité n’est plus une simple case à cocher en matière de conformité, c’est le moteur qui rend possible une IA évolutive. »\u003C/p\u003E\r\n\u003Ch3\u003EDonnées cliniques multimodales\u003C/h3\u003E\r\n\u003Cp\u003EL’IA dans la santé exploite des dossiers structurés, des notes cliniques, des images, des fichiers audio, des formes d’onde, des données génomiques et des documents. Chaque format capture une partie différente de l’histoire du patient ou du parcours opérationnel.\u003C/p\u003E\r\n\u003Cp\u003EUne application d’imagerie médicale peut avoir besoin de l’examen, du rapport de radiologie et des antécédents pertinents. La documentation ambiante commence par l’audio et produit du texte qui doit s’aligner sur les données structurées des consultations. Un modèle de recherche peut combiner des valeurs de laboratoire, des caractéristiques génomiques et des résultats longitudinaux.\u003C/p\u003E\r\n\u003Cp\u003ELa prise en charge de ces combinaisons nécessite bien plus que le simple stockage de plusieurs types de fichiers. La fondation doit préserver les relations entre eux : quelle image appartient à quelle étude, quelle note décrit quelle consultation et quelle observation était d’actualité lors de la génération d’une recommandation.\u003C/p\u003E\r\n\u003Ch3\u003EContexte clinique et sémantique\u003C/h3\u003E\r\n\u003Cp\u003ELe nom d’un champ capture rarement toute la signification clinique d’une valeur. Un diagnostic peut être suspecté, confirmé, historique ou saisi à des fins de facturation. Un médicament peut avoir été prescrit, délivré, administré ou arrêté. Une visite peut représenter une admission à l’hôpital, une mise en observation, des soins d’urgence ou une consultation externe.\u003C/p\u003E\r\n\u003Cp\u003ELe contexte sémantique apporte ces distinctions grâce à des définitions approuvées, des correspondances terminologiques, une logique clinique et des relations. Il prend également en charge des indicateurs gouvernés tels que les réadmissions, la durée de séjour, les lacunes en matière de soins et les mesures de qualité, dont le calcul peut dépendre de règles d’inclusion et d’exclusion détaillées.\u003C/p\u003E\r\n\u003Cp\u003EPour l’IA générative et les agents, le contexte sémantique aide à traduire les questions exprimées dans le langage courant de la santé en données et logiques appropriées. Le système peut récupérer les bons dossiers tout en préservant la signification qui leur est attachée.\u003C/p\u003E\r\n\u003Ch3\u003EServices d’IA, calcul et évaluation\u003C/h3\u003E\r\n\u003Cp\u003ELes applications de santé imposent des exigences différentes à l’environnement d’IA. Les modèles prédictifs peuvent s’entraîner sur de vastes ensembles de données longitudinaux, la \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/computer-vision/\"\u003Evision par ordinateur\u003C/a\u003E nécessite un traitement d’images et un calcul par GPU, et les applications d’IA générative ont besoin d’analyse, de récupération et d’inférence de documents. Les agents ajoutent l’orchestration, l’accès aux outils et l’enregistrement d’activités en plusieurs étapes.\u003C/p\u003E\r\n\u003Cp\u003EL’évaluation doit correspondre au cas d’usage. Un modèle de risque clinique doit être évalué en termes de sensibilité, de spécificité, de calibrage et de performance au sein des sous-groupes de patients. Un assistant de documentation doit être vérifié pour détecter les omissions, les ajouts non justifiés et les implications en matière de codage. Un agent doit être évalué sur sa capacité à sélectionner les bons outils, à respecter les autorisations et à exécuter le workflow avec précision.\u003C/p\u003E\r\n\u003Cp\u003ELa supervision se poursuit après le déploiement. L’évaluation, la traçabilité des données et le versionnage aident les équipes à identifier ces changements et à déterminer si une mise à jour est nécessaire.\u003C/p\u003E\r\n\u003Ch3\u003EGouvernance des données, des modèles et des agents\u003C/h3\u003E\r\n\u003Cp\u003ELa gouvernance dans le secteur de la santé commence par l’accès aux informations de santé protégées (PHI), mais ne s’arrête pas là. Les politiques doivent refléter le rôle de l’utilisateur, la finalité du travail et les informations minimales requises. Le consentement, les restrictions contractuelles et les approbations de recherche peuvent limiter davantage la façon dont les données peuvent être utilisées.\u003C/p\u003E\r\n\u003Cp\u003ELes modèles nécessitent une supervision des données d’entraînement, de l’utilisation prévue, de la performance des sous-groupes et des évolutions au fil du temps. Les applications d’IA générative ont besoin d’un ancrage dans les sources et d’un moyen de distinguer les preuves récupérées du contenu généré. Pour les agents, la gouvernance s’étend aux autorisations et aux actions des outils : la lecture d’une demande de remboursement, la rédaction d’un recours et l’émission d’une décision défavorable représentent différents niveaux d’autorité.\u003C/p\u003E\r\n\u003Cp\u003EL’auditabilité relie ces contrôles. Les organisations de santé doivent être en mesure de déterminer à quelles données une application d’IA a accédé, quel modèle et quelle version ont produit un résultat, quels outils un agent a appelés et à quel endroit une personne a examiné ou approuvé le résultat.\u003C/p\u003E\r\n\u003Cp\u003EEn Europe, les traitements combinant données de santé et prise de décision automatisée, comme les scores de risque clinique, relèvent souvent de l'article 35 du RGPD, qui impose une Analyse d'Impact relative à la Protection des Données (AIPD) avant leur mise en œuvre. Une gouvernance claire des données et des modèles facilite la réalisation de cette analyse et sa mise à jour lorsque le système évolue.\u003C/p\u003E\r\n\u003Ch3\u003ECollaboration gouvernée dans le secteur de la santé\u003C/h3\u003E\r\n\u003Cp\u003ELa prestation de soins et la recherche impliquent des prestataires, des payeurs, des pharmacies, des laboratoires, des agences de santé publique et des organisations des sciences de la vie. De nombreux cas d’usage de l’IA dépendent donc de données détenues en dehors de l’organisation qui crée l’application.\u003C/p\u003E\r\n\u003Cp\u003ELe partage de données gouvernées et les \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/what-is-a-data-clean-room/\"\u003Edata clean rooms\u003C/a\u003E facilitent l’analyse conjointe tout en limitant les informations accessibles à chaque participant. Par exemple, un organisme payeur et un prestataire de soins peuvent évaluer les résultats cliniques, ou des chercheurs peuvent analyser une cohorte inter-établissements sans échanger de données de santé (PHI) brutes et non restreintes.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_what-healthcare-organizations-need-to-scale-ai_0":{"id":"text-34e53e8658","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EASTUCE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EUn dossier patient longitudinal ne constitue pas automatiquement un contexte patient prêt pour l’IA. Le système doit également conserver la date d’enregistrement des informations, leur provenance, leur définition et déterminer si elles sont adaptées à l’utilisation clinique, opérationnelle ou de recherche actuelle.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-snowflake-supports-ai-in-healthcare":{"id":"title-v2-ce167e4774","additionalClasses":"anchor-title anchor-title--how-snowflake-supports-ai-in-healthcare","type":"heading2","lines":["Comment Snowflake prend en charge l’IA dans le secteur de la santé"],":type":"snowflake-site/components/title-v2"},"text_how-snowflake-supports-ai-in-healthcare_0":{"id":"text-217ce7c551","text":"\u003Cp\u003ESnowflake aide les organismes de santé à rassembler les données cliniques, de facturation, d’imagerie, de laboratoire, opérationnelles et de recherche au sein de l’\u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/healthcare-and-life-sciences/\"\u003EAI Data Cloud pour la santé et les sciences de la vie de Snowflake\u003C/a\u003E. La plateforme prend en charge les modèles d’interopérabilité de la santé et les données multimodales, permettant aux équipes de connecter les informations tout au long du parcours patient tout en maintenant la gouvernance sur les dossiers sensibles. Snowflake prend en charge les programmes de conformité pertinents pour les charges de travail de santé réglementées, notamment HIPAA et HITRUST, tandis que chaque organisation reste responsable de la configuration et de l’exploitation de son environnement conformément à ses propres obligations.\u003C/p\u003E\r\n\u003Cp\u003EEn France, Snowflake a obtenu la certification Hébergeur de Données de Santé (HDS) Version 2, un prérequis réglementaire pour héberger et traiter certaines données de santé sur le territoire. Cette certification permet aux établissements de santé, laboratoires pharmaceutiques, organismes de recherche et acteurs de la e-santé d'exploiter l'AI Data Cloud de Snowflake pour leurs charges de travail impliquant des données de santé, dans le respect du cadre réglementaire français et européen.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake Openflow peut ingérer des données en batch, en continu, structurées et non structurées provenant des systèmes de santé. Des guides de développement sont disponibles pour le traitement des messages HL7 v2 et FHIR, aidant ainsi les équipes à travailler avec les formats d’échange de santé les plus utilisés.\u003C/p\u003E\r\n\u003Cp\u003EPour le \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/computer-vision/\"\u003Emachine learning prédictif\u003C/a\u003E et l’imagerie médicale, les équipes peuvent préparer les données et développer des modèles au plus près des dossiers gouvernés qu’elles utilisent. Snowflake Notebooks et Container Runtime prennent en charge la data science avancée et les charges de travail basées sur les GPU ; les ressources de santé actuelles de Snowflake incluent un workflow de traitement d’images médicales distribué conçu avec MONAI.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/features/cortex/\"\u003ECortex AI\u003C/a\u003E prend en charge les applications génératives et multimodales sur les données de santé structurées et non structurées. Les fonctions Cortex AI peuvent extraire, classer et résumer du texte ou des images, tandis que Cortex Search récupère les passages pertinents à partir de documents cliniques, de politiques ou de recherche. Cortex Analyst offre un accès en langage naturel aux données structurées gouvernées par le biais de modèles sémantiques. Ensemble, ces services peuvent ancrer une application à la fois dans le dossier patient ou opérationnel et dans les documents nécessaires à son interprétation.\u003C/p\u003E\r\n\u003Cp\u003EPour les workflows impliquant plusieurs étapes, \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/\"\u003ECortex Agents\u003C/a\u003E peut raisonner sur des données structurées et non structurées et utiliser des outils approuvés. Les décisions cliniques et lourdes de conséquences peuvent rester soumises à un examen humain, l’agent opérant dans le cadre d’autorisations définies.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E offre aux employés du secteur de la santé un environnement de travail conversationnel ancré dans le contexte de l’entreprise et les agents. Il peut aider les utilisateurs à examiner des questions à travers les dossiers, les réclamations, les données en conditions réelles et d’autres données, puis à transférer le travail approuvé vers des applications connectées.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E fournit la gouvernance, le contexte et l’application des politiques à travers les données et l’IA. Les politiques appliquées via le moteur de requête de Snowflake s’appliquent de manière cohérente, que l’appelant soit un analyste, un outil de Business Intelligence ou un agent d’IA, tandis que la traçabilité des données et la découverte aident les équipes à comprendre comment les informations sont utilisées.\u003C/p\u003E\r\n\u003Cp\u003EÀ travers l’écosystème de la santé, Snowflake Secure Data Sharing et \u003Ca href=\"https://www.snowflake.com/fr/product/features/data-clean-rooms/\"\u003ESnowflake Data Clean Rooms\u003C/a\u003E prennent en charge la collaboration gouvernée sans dépendre d’échanges répétés de copies de données statiques. Les prestataires, les organismes payeurs, les chercheurs et les organisations des sciences de la vie peuvent analyser ensemble des données approuvées tout en conservant le contrôle sur les données de santé (PHI) et autres informations sensibles.\u003C/p\u003E\r\n\u003Cp\u003EL’IA dans le secteur de la santé englobe des modèles qui prédisent les risques, des systèmes qui interprètent les images et le langage, et des agents qui coordonnent le travail à travers les applications. Une base partagée donne à ces technologies l’accès au contexte clinique et opérationnel dont elles ont besoin, tout en préservant la surveillance requise lorsque l’IA éclaire les soins, la couverture, la recherche ou le paiement.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-snowflake-supports-ai-in-healthcare_0":{"id":"text-b34778e287","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EÀ RETENIR\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EL'IA dans le secteur de la santé offre le plus de valeur lorsqu'elle est ancrée dans des données connectées et fiables, et déployée avec une gouvernance claire. Les organisations qui combinent contexte longitudinal, interopérabilité, évaluation continue et supervision humaine peuvent mettre à l'échelle l'IA à travers les soins, les opérations et la recherche, tout en gérant les risques cliniques, financiers et liés à la protection des données.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_how-snowflake-supports-ai-in-healthcare_1":{"id":"text-5a748c1844","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/industries/\"\u003EDécouvrez comment l’IA est utilisée dans d’autres secteurs >\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text__0","title_how-ai-supports-clinical-care","text_how-ai-supports-clinical-care_0","card_v2_how-ai-supports-clinical-care_0","title_how-ai-supports-patient-access-and-care-management","text_how-ai-supports-patient-access-and-care-management_0","title_how-ai-supports-healthcare-operations-and-administration","text_how-ai-supports-healthcare-operations-and-administration_0","title_how-ai-supports-healthcare-research","text_how-ai-supports-healthcare-research_0","title_benefits-and-risks-of-ai-in-healthcare","text_benefits-and-risks-of-ai-in-healthcare_0","title_what-healthcare-organizations-need-to-scale-ai","text_what-healthcare-organizations-need-to-scale-ai_0","callout_what-healthcare-organizations-need-to-scale-ai_0","title_how-snowflake-supports-ai-in-healthcare","text_how-snowflake-supports-ai-in-healthcare_0","callout_how-snowflake-supports-ai-in-healthcare_0","text_how-snowflake-supports-ai-in-healthcare_1"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sticky-sidebar","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-c5a1f58559",":type":"snowflake-site/components/container",":items":{"text_943981956_copy_":{"id":"text-53d703ccce","additionalClasses":"eyebrow-text","text":"\u003Cp\u003EDans ce guide\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-c9409a89e5","additionalClasses":"page-toc","text":"\u003Cul\u003E\u003Cli data-anchor=\"how-ai-supports-clinical-care\"\u003EComment l’IA soutient les soins cliniques\u003C/li\u003E\u003Cli data-anchor=\"how-ai-supports-patient-access-and-care-management\"\u003EComment l’IA favorise l’accès des patients et la gestion des soins\u003C/li\u003E\u003Cli data-anchor=\"how-ai-supports-healthcare-operations-and-administration\"\u003EComment l’IA soutient les opérations et l’administration des soins de santé\u003C/li\u003E\u003Cli data-anchor=\"how-ai-supports-healthcare-research\"\u003EComment l’IA soutient la recherche en santé\u003C/li\u003E\u003Cli data-anchor=\"benefits-and-risks-of-ai-in-healthcare\"\u003EAvantages et risques de l’IA dans le secteur de la santé\u003C/li\u003E\u003Cli data-anchor=\"what-healthcare-organizations-need-to-scale-ai\"\u003ECe dont les organismes de santé ont besoin pour mettre à l’échelle l’IA\u003C/li\u003E\u003Cli data-anchor=\"how-snowflake-supports-ai-in-healthcare\"\u003EComment Snowflake prend en charge l’IA dans le secteur de la santé\u003C/li\u003E\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small 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questions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-cd8a483038","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003ELes réponses de nos experts Snowflake à vos questions fréquentes sur l’IA dans le secteur de la santé.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-40ec2b17b8","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"Quels sont les avantages de l’IA dans le secteur de la santé ?","richText":"\u003Cp\u003EL’IA peut aider les organismes de santé à identifier les risques plus tôt, à réduire la charge administrative, à améliorer l’accès des patients, à soutenir des soins plus personnalisés et à mieux utiliser des capacités limitées. Elle peut également aider les équipes à rassembler des informations provenant de systèmes fragmentés, ce qui facilite la compréhension des antécédents d’un patient, la coordination des soins et l’analyse des résultats à l’échelle des populations.\u003C/p\u003E\r\n"},{"title":"Quels sont les risques de l’IA dans la santé ?","richText":"\u003Cp\u003ELes principaux risques incluent des résultats inexacts, des biais, l’exposition des données privées, le manque de transparence, une dépendance excessive à l’automatisation et des changements de performance après le déploiement. Ces risques sont particulièrement importants lorsque l’IA influence les soins cliniques, les paiements, la couverture, l’accès aux services ou la communication avec les patients. Les experts en gouvernance de l’IA dans la santé soulignent généralement que la validation, les contrôles de mise en œuvre, la surveillance et la supervision humaine sont des exigences fondamentales pour une adoption sécurisée. En France, la Haute Autorité de Santé (HAS) souligne qu'un usage de l'IA générative en santé doit rester conscient, supervisé et cliniquement validé, une exigence cohérente avec les principes de supervision humaine décrits ci-dessus.\u003C/p\u003E\r\n"},{"title":"L’IA remplacera-t-elle les médecins et les professionnels de la santé ?","richText":"\u003Cp\u003EL’IA est beaucoup plus susceptible d’accompagner les professionnels de la santé que de les remplacer. De nombreux systèmes d’IA dans la santé sont conçus pour résumer des informations, signaler des tendances, rédiger de la documentation, automatiser des étapes de routine ou prioriser le travail à réviser. Les cliniciens agréés et les employés qualifiés restent essentiels pour interpréter les résultats, formuler des jugements cliniques, résoudre les exceptions et communiquer avec les patients.\u003C/p\u003E\r\n"},{"title":"Comment l’IA aide-t-elle dans l’imagerie médicale ?","richText":"\u003Cp\u003ELes modèles de vision par ordinateur peuvent aider à analyser les radiographies, les scanners, les examens IRM, les images échographiques et les lames d'anatomopathologie. Selon le cas d’usage, l’IA peut signaler une anomalie suspectée, délimiter une zone d’intérêt, comparer des images ou aider à prioriser les examens urgents. Le radiologue ou un autre clinicien agréé continue d’interpréter l’examen et de déterminer la signification clinique du résultat.\u003C/p\u003E\r\n"},{"title":"Comment l’IA générative aide-t-elle les organisations de santé ?","richText":"\u003Cp\u003EL’IA générative peut rédiger des notes cliniques, résumer les antécédents des patients, préparer des résumés de sortie, faciliter les réponses par messagerie sécurisée, expliquer les motifs de refus, récupérer les politiques des organismes de remboursement et aider les employés à traiter de grands volumes de texte. Ces outils sont particulièrement utiles lorsqu’ils s’appuient sur des données sources fiables et sont conçus de manière à ce que les utilisateurs puissent vérifier la provenance des informations.\u003C/p\u003E\r\n"}],":type":"snowflake-site/components/simple-snowflake-accordion"}},":itemsOrder":["simple_snowflake_acc"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1467213961":{"id":"flexible-column-container-6f710df892","propertiesId":"hub-explore-resources-header","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"large","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-843f076bc9",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2":{"id":"title-v2-41d019234b","additionalClasses":"hub-explore-resources-header__headline","type":"heading2","lines":["Explorez les 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soins"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip","appliedCssClassNames":"snowflake-content-chip-white-bg"},"resource_chip_2":{"id":"content-chip-9abc1b5c06","tagText":"BLOG","tagColor":"#29B5E8","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/ai-agents-in-healthcare-snowflake-summit-takeaways/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Lire la suite"},"headline":{"id":"title","type":"heading5","lines":["Agents d’IA dans la santé : 3 enseignements du Snowflake 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vie"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip","appliedCssClassNames":"snowflake-content-chip-white-bg"}},":itemsOrder":["resource_chip_0","resource_chip_1","resource_chip_2","resource_chip_3"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1158003461":{"id":"flexible-column-container-efaf3a3750","propertiesId":"hub-explore-topics-header","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"large","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-0dba162e5d",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2_copy_copy":{"id":"title-v2-30828bb709","additionalClasses":"hub-explore-topics-header__headline","type":"heading2","lines":["Explorez les sujets liés à l’IA"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy":{"id":"text-dbb02b7df3","additionalClasses":"hub-explore-topics-header__subheadline","text":"\u003Cp\u003EUne analyse approfondie de chaque aspect de l’intelligence artificielle\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy_copy","text_copy_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-white-bg"},"flexible_column_cont_1377146023":{"id":"flexible-column-container-7b667d6a4e","propertiesId":"hub-explore-topics-grid","type":"3-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-2b8d3d0ea4",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"topic_card_0":{"id":"card-v2-d6112afdb4","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["L’IA 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