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looking into a microscope","isLcpImage":false,"width":"2120",":type":"snowflake-site/components/image"},"timeToRead":"8","publicationDate":"OCT 15, 2026","tag":{"tagText":"Healthcare e life sciences","tagColor":"#29B5E8"},"title":{"lines":["Agentic AI in oncologia: dalla scoperta alla remissione"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"],":type":"snowflake-site/components/container"},"responsivegrid_content":{"columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"blog_text":"aem-GridColumn aem-GridColumn--default--12"},"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-8157e3297a","text":"\u003Cp\u003EDa decenni ricercatori e team clinici affrontano la sfida del cancro al seno e di altri tumori, ma oggi c’è qualcosa di davvero diverso nel modo in cui le organizzazioni combattono il cancro. Ho passato del tempo con ricercatori in ambito oncologico, sistemi sanitari e aziende life sciences e, nella maggior parte dei casi, il discorso si è spostato da “Abbiamo molti dati” e “Come organizziamo i nostri dati?” a “Cosa possono fare i nostri sistemi con questi dati?”\u003C/p\u003E\n\u003Cp\u003EL’oncologia genera dati più eterogenei e critici di quasi qualsiasi altro ambito. Sequenze genomiche, immagini patologiche, note cliniche, dati assicurativi e segnali provenienti da dispositivi wearable hanno storicamente giocato contro i clinici piuttosto che a loro favore: frammentati tra i vari sistemi e lenti da assemblare proprio quando le decisioni contano di più.\u003C/p\u003E\n\u003Cp\u003EL’Agentic AI (sistemi che ragionano, pianificano e agiscono attraverso flussi di lavoro multifase) sta iniziando a colmare questo divario lungo tutto il percorso oncologico, portando l’Healthcare e le Life Sciences dai dati isolati alla sperimentazione con l’AI e, in prospettiva, a un impatto misurabile. Questo articolo segue il percorso dal laboratorio alla sopravvivenza, evidenziando le organizzazioni che stanno portando avanti questo lavoro.\u003C/p\u003E\n\u003Ch3\u003ECinque fasi, cinque colli di bottiglia\u003C/h3\u003E\n\u003Cp\u003EIl percorso oncologico si compone di cinque fasi collegate tra loro, ciascuna con un problema di dati di lunga data. I team di discovery e ricerca si affidano a set di dati molecolari e genomici che restano isolati. Lo screening e la diagnosi precoce riuniscono solo con difficoltà immagini, biomarcatori, dati delle cartelle cliniche elettroniche (EHR) e anamnesi familiare. La diagnosi e la pianificazione del trattamento spesso richiedono che i tumor board assemblino i profili dei pazienti manualmente e in modo incompleto. Il trattamento e il coordinamento delle cure dipendono da dati multimodali del mondo reale, frammentati tra sedi e sistemi diversi. La remissione e la sopravvivenza generano evidenze che possono arrivare con anni di ritardo rispetto alle necessità cliniche.\u003C/p\u003E\n\u003Cp\u003EQuesti problemi si aggravano a vicenda. Lo stesso problema di dati che rallenta la discovery può ritardare l’arrivo del paziente giusto allo studio clinico giusto. Tutti e cinque i colli di bottiglia hanno la stessa causa di fondo: non esiste una data foundation unificata in grado di ragionare lungo tutto il percorso.\u003C/p\u003E\n\u003Ch3\u003ECosa significa davvero lavorare con l’Agentic AI e perché partire dalla data foundation\u003C/h3\u003E\n\u003Cp\u003EL’AI tradizionale risponde a una domanda. L’Agentic AI pianifica ed esegue più passaggi senza che sia necessario l’intervento umano a ogni fase.\u003C/p\u003E\n\u003Cp\u003ETuttavia, l’AI è efficace solo quanto la data foundation che la governa. I dati oncologici non sono un foglio di calcolo. Sequenze genomiche, immagini patologiche, note non strutturate, dati assicurativi e segnali da dispositivi wearable sono per natura complessi e multimodali. Racchiudono informazioni preziose necessarie per far progredire la cura, ma solo se le organizzazioni riescono a importarli, unificarli e ragionare su di essi nel loro insieme. Una variante genomica necessita di un contesto clinico. Un riscontro diagnostico per immagini acquisisce significato se affiancato a marcatori molecolari e anamnesi terapeutica.\u003C/p\u003E\n\u003Cp\u003EPensa a un agente AI che riceve un risultato di sequenziamento, lo confronta con profili di pazienti simili, identifica i trial open, modella la fattibilità e prepara una sintesi per il tumor board, tutto a partire da un singolo evento di dati. La convergenza tra infrastrutture dati multimodali, foundation model e piattaforme AI governate rende tutto questo operativamente realizzabile.\u003C/p\u003E\n\u003Cp\u003EMa le capacità agentiche che operano su dati e flussi di lavoro regolamentati richiedono un livello di controllo differente. La governance e la sicurezza devono essere integrate nella strategia agentica fin dall’inizio (non aggiunte successivamente), per proteggere le informazioni sanitarie protette (PHI) e garantire la conformità a standard di settore come l’HIPAA. L’impatto di una risposta errata in ambito clinico comporta implicazioni che vanno ben oltre quelle di una dashboard sbagliata, e può influire sugli esiti di salute per i pazienti. Ciò significa che tracciabilità, controlli di accesso, guardrail per gli agenti AI e regole chiare su quando mantenere il controllo umano sono elementi mission critical, da considerare fin dalle prime fasi del percorso con l’Agentic AI.\u003C/p\u003E\n\u003Ch3\u003EDove l’Agentic AI sta creando un impatto reale\u003C/h3\u003E\n\u003Ch4\u003EDiscovery e ricerca\u003C/h4\u003E\n\u003Cp\u003EIl primo collo di bottiglia (dati molecolari e genomici isolati) è dove l’Agentic AI sta mostrando le prime promesse. Lo sviluppo di farmaci oncologici ha un tasso di abbandono persistentemente superiore al 95%, con una spesa stimata tra 50 e 60 miliardi di dollari all’anno per trial oncologici falliti, secondo \u003Ca href=\"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2807710\" target=\"_blank\"\u003Euno studio del 2023 pubblicato su JAMA Network Open\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003EI sistemi agentici possono attraversare dati genomici, strutturali e di letteratura scientifica in un’unica catena di ragionamento. Possono individuare in anticipo candidati validi, segnalare probabili modalità di fallimento prima della fase di laboratorio e integrare continuamente nuove scoperte. Il risultato pratico è un percorso più rapido dall’ipotesi al composto valido, che offre ai team di ricerca più tempo da dedicare alle opportunità più promettenti.\u003C/p\u003E\n\u003Ch4\u003EScreening e diagnosi precoce\u003C/h4\u003E\n\u003Cp\u003EIl secondo collo di bottiglia (segnali multimodali che non convergono mai) crea un problema profondamente umano. Dopo una biopsia, i pazienti possono trascorrere da due a sei settimane in attesa dei risultati patologici, con ritardi ancora maggiori quando sono necessari test sui biomarcatori molecolari o il sequenziamento genomico. Durante questo periodo, restano nell’incertezza, incapaci di pianificare o di dare un senso a ciò che verrà, senza sapere se risulteranno positivi o negativi alla malattia.\u003C/p\u003E\n\u003Cp\u003EL’AI sta comprimendo parte di questi flussi di lavoro. Il triage per immagini basato sull’AI può ridurre i tempi di risposta da giorni a minuti, dando priorità ai risultati più critici in termini di tempo; la patologia digitale può trasformare l’analisi dei vetrini, accelerando flussi di lavoro precedentemente manuali grazie alla scienza computazionale. Un ampio studio di screening condotto negli Stati Uniti in condizioni reali ha rilevato che un flusso di lavoro supportato dall’AI ha identificato \u003Ca href=\"https://www.nature.com/articles/s44360-025-00001-0\" target=\"_blank\"\u003Eil 21,6% in più di tumori al seno rispetto alla revisione standard della mammografia\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003EL’opportunità offerta dall’approccio agentico sta nella convergenza: un riscontro per immagini, un risultato di biomarcatore, un segnale di rischio nella cartella clinica elettronica (EHR) e uno schema di anamnesi familiare considerati insieme, al posto dell’assemblaggio manuale sequenziale che genera l’attesa. L’impatto clinico è una diagnosi più rapida. L’impatto umano sono meno settimane nel limbo.\u003C/p\u003E\n\u003Ch4\u003EDiagnosi e pianificazione del trattamento\u003C/h4\u003E\n\u003Cp\u003EI tumor board restano tra i flussi di lavoro più importanti e più manuali dell’oncologia. I clinici assemblano profili genomici, dati patologici, immagini e anamnesi terapeutiche provenienti da sistemi eterogenei, spesso sotto forte pressione di tempo.\u003C/p\u003E\n\u003Cp\u003EUn sistema agentico attivato da un nuovo risultato diagnostico può raccogliere queste fonti, strutturare un dossier sul paziente, verificare l’eleggibilità ai trial e rendere disponibile il materiale prima che il board si riunisca. Prepara il terreno per il giudizio clinico e offre agli specialisti un punto di partenza più completo per la discussione, invece di partire da zero.\u003C/p\u003E\n\u003Cp\u003EL’impatto umano qui è concreto: meno tempo in attesa di risposte e tempi più rapidi per arrivare al trattamento.\u003C/p\u003E\n\u003Ch4\u003ETrattamento e coordinamento delle cure\u003C/h4\u003E\n\u003Cp\u003EIl quarto collo di bottiglia (dati multimodali del mondo reale frammentati) contribuisce a uno dei problemi persistenti dell’oncologia: I pazienti che potrebbero beneficiare dei trial clinici spesso non si iscrivono mai.\u003C/p\u003E\n\u003Cp\u003EI flussi di lavoro agentici possono identificare coorti eleggibili in tempo quasi reale, anche con criteri di trial complessi. Possono inoltre modellare la fattibilità del trial, le tempistiche di iscrizione, la capacità dei siti e la complessità del protocollo prima dell’avvio dello studio, quando gli sponsor hanno ancora tempo per agire. Questo fa passare le operazioni dei trial da un’iscrizione reattiva a un matching proattivo.\u003C/p\u003E\n\u003Ch4\u003ERemissione e sopravvivenza\u003C/h4\u003E\n\u003Cp\u003EL’ultimo collo di bottiglia (la generazione di evidenze in ritardo rispetto alle necessità cliniche) interessa enti regolatori, payer, sistemi sanitari e pazienti. I flussi di lavoro agentici possono strutturare continuamente evidenze del mondo reale a partire da dati di trattamento, segnalazioni di eventi avversi e segnali di sopravvivenza a lungo termine, trasformando la generazione di evidenze da un esercizio retroattivo e occasionale a un processo proattivo, continuo e trasformato in prodotto per operare su larga scala. Questi stessi flussi di lavoro possono monitorare segnali di adesione come le modalità di rinnovo delle prescrizioni, i sintomi riportati dai pazienti e gli appuntamenti di follow-up mancati, segnalando i pazienti a rischio di interrompere terapie a lungo termine come il trattamento endocrino, dove un intervento precoce può influire in modo significativo sui tassi di recidiva.\u003C/p\u003E\n\u003Cp\u003EQuesto lavoro può comprimere il percorso dal trattamento all’insight, facendolo passare da una scala di anni verso una scala di mesi, in base alla disponibilità dei dati, al disegno dello studio e alla governance. Chiude inoltre il cerchio: Le evidenze del mondo reale e gli schemi di adesione generati durante la fase di sopravvivenza possono orientare il prossimo programma di discovery, il disegno dei trial e la strategia terapeutica, contribuendo al contempo a ridurre il rischio di recidiva per i pazienti ancora in cura.\u003C/p\u003E\n\u003Ch3\u003ELe organizzazioni che stanno transformando l’AI in realtà in ambito sanitario\u003C/h3\u003E\n\u003Ch4\u003EAstraZeneca\u003C/h4\u003E\n\u003Cp\u003EUtilizzando \u003Ca href=\"https://www.snowflake.com/en/product/features/cortex/\" target=\"_blank\"\u003ESnowflake Cortex AI\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/en/resources/ebook/7-ways-ai-data-on-snowflake-helps-drive-better-patient-and-business-outcomes-in-healthcare-and-life-sciences/thankyou/\" target=\"_blank\"\u003EAstraZeneca\u003C/a\u003E ha dichiarato di aver accelerato notevolmente la creazione dei propri data product, riducendo i tempi di sviluppo da sei mesi con 16 ingegneri a soli quattro giorni con due. Questo guadagno di efficienza ha portato a più di 118 data product, liberando migliaia di ore di produttività e oltre 10 milioni di dollari di risparmi. Soprattutto, questi progressi alimentano innovazioni basate sull’AI come le radiografie del torace potenziate dall’AI, pensate per migliorare la diagnosi precoce delle malattie polmonari e, in alcuni casi, associate a un aumento del tasso di sopravvivenza fino al 90%. Con i processi sui dati ottimizzati su larga scala, AstraZeneca è nella posizione ideale per guidare ulteriore innovazione e offrire migliori esiti di salute per i pazienti nel settore delle life sciences.\u003C/p\u003E\n\u003Ch4\u003EZelis Healthcare\u003C/h4\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/it/blog/ai-agents-healthcare-efficiency/\" target=\"_blank\"\u003EZelis Healthcare\u003C/a\u003E mostra il passo successivo: il passaggio da una data foundation unificata ad agenti AI in produzione. Zelis ha migrato l’intera data foundation su Snowflake e ora acquisisce dati in tempo quasi reale da oltre 40 fonti tramite \u003Ca href=\"https://www.snowflake.com/en/product/features/openflow/\" target=\"_blank\"\u003ESnowflake Openflow\u003C/a\u003E. Con questa data foundation attiva, il team ha implementato più di 20 agenti AI in produzione, costruendo agenti AI proof-of-concept in un solo giorno e portandoli in piena produzione due giorni dopo, fornendo risposte in tempo quasi reale e riducendo il carico sul team dati principale. Lakshman Pindikur, Senior Director of Data Engineering (Zelis Data Cloud) di Zelis Healthcare, descrive la data foundation alla base di questo progresso: “Possiamo abilitare rapidamente i nostri utenti grazie a Snowflake CoWork, con Openflow come strumento di riferimento per portare tutti i dati in Snowflake, e Horizon Catalog per la sicurezza e la governance di livello enterprise.”\u003C/p\u003E\n\u003Cp\u003ELo schema più ampio conta più di qualsiasi singola implementazione. Le organizzazioni di ricerca stanno riunendo dati clinici, genomici e di imaging. I provider stanno collegando le anamnesi terapeutiche con le operazioni dei trial. I team di evidenze del mondo reale e di health economics and outcomes research (HEOR) stanno combinando dati assicurativi, EHR, registri e dati generati dai pazienti per rispondere più rapidamente alle domande. L’Agentic AI offre a queste organizzazioni un modo per coordinare il lavoro tra i diversi sistemi, mantenendo gli esperti al controllo delle decisioni.\u003C/p\u003E\n\u003Ch3\u003EIl lavoro è già in corso\u003C/h3\u003E\n\u003Cp\u003ELa tecnologia in astratto conta meno di ciò che le organizzazioni ne stanno facendo in questo momento: trasformare l’Agentic AI da esperimento a strumento per ottenere crescita ed efficienza misurabili lungo tutto il percorso oncologico. Lo fanno collegando evidenze frammentate, supportando i ricercatori, migliorando la pianificazione dei trial e offrendo ai clinici una visione più completa del percorso di ciascun paziente.\u003C/p\u003E\n\u003Cp\u003EPer scoprire come le organizzazioni stanno guidando il progresso in questo ambito, leggi come \u003Ca href=\"https://www.icf.com/clients/health/nci-ai-ready-data-snowflake\" target=\"_blank\"\u003Eil National Cancer Institute (NCI)\u003C/a\u003E ha modernizzato la propria piattaforma dati aziendale per ottenere maggiore flessibilità, migliorare la trasparenza dei costi e supportare l’innovazione futura.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 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