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진료팀은 수십 년 동안 유방암을 비롯한 다양한 암이라는 난제에 맞서 왔습니다. 그러나 오늘날 조직이 암에 대응하는 방식에는 분명한 변화가 일어나고 있습니다. 저는 종양학 연구자, 의료기관 네트워크, 라이프사이언스 기업들과 시간을 함께해 왔습니다. 그 과정에서 대화의 중심은 ‘우리는 데이터가 많다’, ‘데이터를 어떻게 정리할 것인가’에서 ‘우리 시스템은 이 데이터로 무엇을 할 수 있는가’로 확연히 이동했습니다.\u003C/p\u003E\n\u003Cp\u003E종양학은 거의 어떤 도메인보다도 이질적이고 중대한 의사 결정에 직결되는 데이터를 생성합니다. 유전체 서열, 병리 이미지, 임상 노트, 청구 기록, 웨어러블 신호는 오랫동안 여러 시스템에 흩어져 있었습니다. 의사 결정이 가장 중요한 순간에 이를 모으는 속도도 느려, 임상의에게 도움이 되기보다 부담으로 작용해 왔습니다.\u003C/p\u003E\n\u003Cp\u003E여러 단계의 워크플로우 전반에서 추론하고 계획하며 실행하는 시스템인 에이전틱 AI는 암 치료 전 주기에서 이러한 격차를 좁히기 시작했습니다. 헬스케어 및 라이프사이언스는 사일로화된 데이터에서 AI 실험으로, 더 나아가 측정 가능한 임팩트로 이동하고 있습니다. 이 게시물은 연구실에서 암 생존자 관리에 이르는 여정을 따라가며, 이러한 변화를 앞당기고 있는 조직들을 조명합니다.\u003C/p\u003E\n\u003Ch3\u003E5단계, 5가지 오래된 병목\u003C/h3\u003E\n\u003Cp\u003E암 치료 전 주기는 서로 연결된 5단계로 구성되며, 각 단계에는 오래된 데이터 문제가 존재합니다. 신약 발견 및 연구팀은 여전히 사일로화된 분자 및 유전체 데이터 세트에 의존합니다. 검진과 조기 발견 단계에서는 영상, 바이오마커, 전자 건강 기록(EHR) 데이터, 가족 병력 기록을 함께 활용해야 하지만 이를 통합하기가 쉽지 않습니다. 진단과 치료 계획 단계에서는 다학제 진료팀이 환자 프로필을 수작업으로 취합해야 하는 경우가 많으며, 이렇게 취합한 정보는 불완전한 경우가 많습니다. 치료와 진료 조율은 여러 기관과 시스템에 분산된 멀티모달 실사용 데이터에 의존합니다. 관해와 암 생존자 관리 단계에서 생성되는 근거는 임상적 필요보다 몇 년씩 뒤처질 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E이 문제들은 서로 누적됩니다. 신약 발견을 늦추는 데이터 실패는 적합한 환자가 적합한 임상시험에 도달하는 시점까지 지연시킬 수 있습니다. 다섯 가지 병목은 모두 같은 근본 원인으로 수렴합니다. 전 주기를 가로질러 추론할 수 있는 통합 데이터 파운데이션이 없다는 점입니다.\u003C/p\u003E\n\u003Ch3\u003E여기서 말하는 ‘에이전틱’의 의미와 데이터 파운데이션 우선 접근법\u003C/h3\u003E\n\u003Cp\u003E기존 AI는 질문에 답합니다. 에이전틱 AI는 사람이 각 단계를 일일이 실행하지 않아도 여러 단계에 걸쳐 계획하고 수행합니다.\u003C/p\u003E\n\u003Cp\u003E하지만 AI의 효과는 이를 관리하고 통제하는 데이터 파운데이션의 수준을 넘어설 수 없습니다. 암 데이터는 단순한 스프레드시트가 아닙니다. 유전체 서열, 병리 이미지, 비정형 노트, 청구 기록, 웨어러블 신호는 본질적으로 복잡한 멀티모달 데이터입니다. 이 데이터에는 진료를 발전시키는 데 필요한 중요한 인사이트가 담겨 있습니다. 다만 조직이 이를 함께 수집하고 통합하며 추론할 수 있을 때에만 그 가치가 실현됩니다. 유전체 변이는 임상적 맥락이 있어야 의미를 갖습니다. 영상 소견도 분자 마커와 치료 기록이 함께 놓일 때 의미가 분명해집니다.\u003C/p\u003E\n\u003Cp\u003E예를 들어 하나의 에이전트가 시퀀싱 결과를 수신하고, 이를 유사한 환자 프로필과 비교하며, 참여자를 모집 중인 임상시험을 식별하고, 실행 가능성을 모델링한 뒤, 다학제 진료회의용 요약까지 준비한다고 가정해 보겠습니다. 이 모든 과정은 하나의 데이터 이벤트에서 시작됩니다. 멀티모달 데이터 인프라, 파운데이션 모델, 거버넌스가 적용된 AI 플랫폼의 융합은 이러한 운영 방식을 현실화하고 있습니다.\u003C/p\u003E\n\u003Cp\u003E하지만 규제 대상 데이터와 워크플로우에서 작동하는 에이전틱 역량에는 훨씬 높은 수준의 검토가 필요합니다. 보호 대상 건강 정보(PHI)를 안전하게 보호하고 HIPAA와 같은 업계 표준에 대한 규정 준수를 지원하려면, 거버넌스와 보안을 에이전틱 전략의 처음부터 내장해야 합니다. 나중에 덧붙이는 방식이어서는 안 됩니다. 임상 맥락에서 잘못된 답변이 미치는 영향은 잘못된 대시보드의 문제를 훨씬 넘어 환자 결과에까지 영향을 줄 수 있습니다. 따라서 감사 가능성, 액세스 제어, 에이전트 가드레일, 사람의 개입을 유지해야 하는 시점에 대한 명확한 규칙은 필수적입니다. 이는 에이전틱 AI 도입 초기부터 고려해야 합니다.\u003C/p\u003E\n\u003Ch3\u003E에이전틱 AI가 실제 임팩트를 만드는 영역\u003C/h3\u003E\n\u003Ch4\u003E신약 발견 및 연구\u003C/h4\u003E\n\u003Cp\u003E첫 번째 병목인 사일로화된 분자 및 유전체 데이터 영역에서 에이전틱 AI는 초기 가능성을 보여주고 있습니다. 종양학 신약 개발은 지속적으로 95%가 넘는 탈락률을 보이고 있으며, \u003Ca href=\"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2807710\" target=\"_blank\"\u003EJAMA Network Open의 2023년 연구\u003C/a\u003E에 따르면 실패한 종양학 임상시험에 매년 약 500억~600억 달러가 지출되는 것으로 추정됩니다.\u003C/p\u003E\n\u003Cp\u003E에이전틱 시스템은 하나의 추론 체인 안에서 유전체, 구조, 문헌 데이터를 트래버스할 수 있습니다. 이를 통해 더 이른 단계에서 유망 후보를 찾아내고, 실험 전에 발생 가능성이 높은 실패 유형을 표시하며, 새로운 연구 결과를 지속적으로 반영할 수 있습니다. 실질적인 결과는 가설에서 유망 화합물로 이어지는 경로가 빨라진다는 것입니다. 연구팀은 가장 가능성 높은 기회에 더 많은 시간을 집중할 수 있습니다.\u003C/p\u003E\n\u003Ch4\u003E검진 및 조기 발견\u003C/h4\u003E\n\u003Cp\u003E두 번째 병목인 통합되지 못하는 멀티모달 신호는 환자의 삶에 깊이 영향을 미치는 문제를 만들어 냅니다. 생검 후 환자는 병리 결과를 기다리는 데 2~6주를 보낼 수 있으며, 분자 바이오마커 검사나 유전체 시퀀싱이 필요할 경우 지연은 더 길어집니다. 그 기간 동안 환자는 질병 양성인지 음성인지 알지 못한 채 불확실성 속에 놓입니다. 다음 단계를 계획하거나 이해하기도 어렵습니다.\u003C/p\u003E\n\u003Cp\u003EAI는 이러한 워크플로우의 일부 단계에 걸리는 시간을 단축하고 있습니다. AI 기반 영상 트리아지는 시간 민감도가 높은 소견을 우선순위화해 처리 시간을 며칠에서 몇 분으로 줄일 수 있습니다. 디지털 병리학은 슬라이드 분석을 혁신하며, 과거 수작업으로 진행되던 워크플로우를 계산 과학으로 가속화할 수 있습니다. 미국의 대규모 실제 검진 연구에서는 AI 지원 워크플로우가 \u003Ca href=\"https://www.nature.com/articles/s44360-025-00001-0\" target=\"_blank\"\u003E표준 유방촬영술 판독보다 유방암을 21.6% 더 많이 식별했다\u003C/a\u003E는 결과가 나왔습니다.\u003C/p\u003E\n\u003Cp\u003E에이전틱 기회는 수렴에 있습니다. 영상 소견, 바이오마커 결과, EHR 위험 플래그, 가족 병력 기록의 패턴을 함께 고려함으로써 대기 시간을 만들어 내는 순차적 수작업 조합을 대체할 수 있습니다. 임상적 효과는 더 빠른 진단입니다. 환자에게는 불확실성 속에서 보내는 시간이 줄어든다는 의미입니다.\u003C/p\u003E\n\u003Ch4\u003E진단 및 치료 계획\u003C/h4\u003E\n\u003Cp\u003E다학제 진료회의는 여전히 종양학에서 가장 중요하면서도 가장 수작업이 많은 워크플로우 중 하나입니다. 임상의는 강한 시간 압박 속에서 여러 시스템에 흩어진 유전체 프로필, 병리, 영상, 치료 기록을 조합하는 경우가 많습니다.\u003C/p\u003E\n\u003Cp\u003E새로운 진단 결과로 트리거되는 에이전틱 시스템은 이러한 소스를 수집하고, 환자 요약 정보를 구조화하며, 임상시험 참여 자격을 대조해 확인하고, 다학제 진료회의가 열리기 전에 필요한 자료를 제시할 수 있습니다. 이는 임상적 판단의 기반을 마련하고, 전문가들이 처음부터 다시 시작하는 대신 더 완전한 출발점에서 논의를 시작할 수 있게 합니다.\u003C/p\u003E\n\u003Cp\u003E여기서 사람에게 미치는 영향은 분명합니다. 답을 기다리는 시간은 줄고 치료 시작은 빨라집니다.\u003C/p\u003E\n\u003Ch4\u003E치료 및 진료 조율\u003C/h4\u003E\n\u003Cp\u003E네 번째 병목인 파편화된 멀티모달 실사용 데이터는 종양학에서 지속적으로 발생하는 다음 문제의 원인 중 하나입니다. 임상시험의 혜택을 받을 수 있는 환자들이 종종 등록까지 이르지 못한다는 점입니다.\u003C/p\u003E\n\u003Cp\u003E에이전틱 워크플로우는 복잡한 임상시험 기준 전반에서 적격 코호트를 준실시간으로 식별할 수 있습니다. 또한 임상시험이 시작되기 전, 임상시험 의뢰자가 아직 조치를 취할 수 있는 시점에 시험 실행 가능성, 등록 일정, 기관 수용력, 프로토콜 복잡성을 모델링할 수 있습니다. 이를 통해 임상시험 운영은 사후 대응식 등록에서 선제적 매칭으로 전환됩니다.\u003C/p\u003E\n\u003Ch4\u003E관해 및 암 생존자 관리\u003C/h4\u003E\n\u003Cp\u003E마지막 병목인 임상적 필요보다 뒤처지는 근거 생성은 규제 기관, 의료비 지불 기관, 의료기관 네트워크, 환자 모두에 영향을 미칩니다. 에이전틱 워크플로우는 치료 데이터, 이상 사례 피드, 장기 생존 신호에서 실사용 근거를 지속적으로 구조화할 수 있습니다. 이를 통해 근거 생성은 사후적이고 일회성인 작업에서 대규모 활용을 위해 제품화된 선제적이고 지속적인 프로세스로 전환됩니다. 동일한 워크플로우는 처방약 재조제 패턴, 환자 보고 증상, 추적 진료 불참과 같은 치료 순응도 신호를 모니터링하고, 내분비 치료와 같은 장기 치료를 중단할 위험이 있는 환자를 표시할 수 있습니다. 이러한 경우 조기 개입은 재발률에 의미 있는 영향을 미칠 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E이러한 작업은 데이터 가용성, 연구 설계, 거버넌스에 따라 치료에서 인사이트로 이어지는 경로를 몇 년에서 몇 개월 수준으로 단축할 수 있습니다. 또한 이 과정은 루프를 완성합니다. 암 생존자 관리 과정에서 생성된 실사용 근거와 순응도 패턴은 다음 신약 발견 프로그램, 임상시험 설계, 치료 전략에 반영될 수 있습니다. 동시에 여전히 진료를 받고 있는 환자의 재발 위험을 낮추는 데도 도움이 됩니다.\u003C/p\u003E\n\u003Ch3\u003E헬스케어에서 AI를 현실로 만드는 조직들\u003C/h3\u003E\n\u003Ch4\u003EAstraZeneca\u003C/h4\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/cortex/\" target=\"_blank\"\u003ESnowflake Cortex AI\u003C/a\u003E를 사용한 \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\u003E는 데이터 제품 생성 속도를 크게 높였다고 밝혔습니다. 개발 기간은 엔지니어 16명이 6개월 걸리던 수준에서 엔지니어 2명이 단 4일 만에 완료하는 수준으로 단축되었습니다. 이러한 효율성 향상으로 118개가 넘는 데이터 제품을 만들고, 업무에 드는 시간을 수천 시간 줄이며, 1,000만 달러가 넘는 비용을 절감했습니다. 특히 이러한 발전은 AI 기반 흉부 X선과 같은 AI 주도 혁신을 뒷받침합니다. 이는 폐 질환의 조기 발견을 개선하는 것을 목표로 하며, 일부 사례에서는 최대 90%의 생존율 증가와 관련이 있는 것으로 나타났습니다. 대규모로 최적화된 데이터 프로세스를 기반으로 AstraZeneca는 라이프사이언스 부문에서 더 많은 혁신을 추진하고 환자 결과를 개선할 수 있는 유리한 위치에 있습니다.\u003C/p\u003E\n\u003Ch4\u003EZelis Healthcare\u003C/h4\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ko/blog/ai-agents-healthcare-efficiency/\" target=\"_blank\"\u003EZelis Healthcare\u003C/a\u003E는 통합된 파운데이션에서 프로덕션 에이전트로 나아가는 다음 단계를 보여줍니다. Zelis는 전체 데이터 파운데이션을 Snowflake로 이전했으며, 현재 \u003Ca href=\"https://www.snowflake.com/ko/product/features/openflow/\" target=\"_blank\"\u003ESnowflake Openflow\u003C/a\u003E를 통해 40개가 넘는 소스에서 준실시간 데이터를 수집하고 있습니다. 이러한 기반 위에서 Zelis 팀은 20개가 넘는 프로덕션 에이전트를 배포했습니다. PoC(Proof of Concept) 에이전트를 하루 만에 구축하고 이틀 뒤 본격적인 운영에 투입해, 준실시간 답변을 제공하고 핵심 데이터 팀의 부담을 줄였습니다. Zelis Healthcare의 데이터 엔지니어링 선임 이사(Zelis Data Cloud)인 Lakshman Pindikur는 이러한 진전을 가능하게 한 기반을 다음과 같이 설명합니다. “Snowflake CoWork를 통해 사용자를 빠르게 지원할 수 있습니다. 모든 데이터를 Snowflake로 가져오는 핵심 도구로 Openflow를 사용하고, 엔터프라이즈급 보안과 거버넌스를 위해 Horizon Catalog를 활용합니다.”\u003C/p\u003E\n\u003Cp\u003E개별 구현보다 더 중요한 것은 더 큰 패턴입니다. 연구 조직들은 임상, 유전체, 영상 데이터를 통합하고 있습니다. 의료 제공자는 치료 기록과 임상시험 운영을 연결하고 있습니다. 실사용 근거 팀과 보건경제 및 성과 연구(HEOR) 팀은 청구, EHR, 레지스트리, 환자 생성 데이터를 결합해 질문에 더 빠르게 답하고 있습니다. 에이전틱 AI는 전문가가 의사 결정의 통제권을 유지한 상태에서, 이러한 조직들이 시스템 전반의 작업을 조율할 수 있는 방법을 제공합니다.\u003C/p\u003E\n\u003Ch3\u003E이미 진행 중인 변화\u003C/h3\u003E\n\u003Cp\u003E중요한 것은 추상적인 기술 자체보다 조직이 지금 그것으로 무엇을 하고 있는가입니다. 이들은 에이전틱 AI를 실험 단계에서 암 치료 여정 전반의 측정 가능한 성장과 효율성으로 전환하고 있습니다. 조직들은 파편화된 근거를 연결하고, 연구자를 지원하며, 임상시험 계획을 개선하고, 임상의가 각 환자의 여정을 더 온전히 파악할 수 있도록 함으로써 이를 실현하고 있습니다.\u003C/p\u003E\n\u003Cp\u003E이 분야에서 조직들이 어떻게 진전을 만들어 가고 있는지 확인하려면, \u003Ca href=\"https://www.icf.com/clients/health/nci-ai-ready-data-snowflake\" target=\"_blank\"\u003E미국 국립암연구소(NCI)\u003C/a\u003E가 유연성을 높이고 비용 투명성을 개선하며 미래 혁신을 지원하기 위해 엔터프라이즈 데이터 플랫폼을 현대화한 방식을 살펴보세요.\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 aem-Grid--default--12","columnClassNames":{},"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"author_chip":"aem-GridColumn 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