의료 분야에서 인간-AI 협업 우선순위: 신뢰할 수 있는 거버넌스, 실제 가치, 통합 적응형 배포를 위한 TRIAD 프레임워크

  • role: First author第一作者
  • Affiliation:

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Jia Li1,  
  • Affiliation:

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Zi-Chun Zhou1,  
  • role: Corresponding author通信作者
  • Affiliation:

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

    Department of Health Data Science, School of Medical Technology, Capital Medical University, Beijing 100050, China

    Precision and Intelligence Medical Imaging Lab, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

  • Email:cjr.wzhch@vip.163.com
  • Introduction:* Zhen-Chang Wang cjr.wzhch@vip.163.com
Zhen-Chang Wang123*,  
  • role: Corresponding author通信作者
  • Affiliation:

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

    Department of Health Data Science, School of Medical Technology, Capital Medical University, Beijing 100050, China

    Precision and Intelligence Medical Imaging Lab, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

  • Email:chrislvhan@126.com
  • Introduction:Han Lv chrislvhan@126.com
Han Lv123*

추상적인

인공지능(AI)과 빅데이터가 의료 환경을 재구성하고 있습니다. 그러나 임상 가치는 시스템이 임상의들을 얼마나 잘 보조하고 일상적인 작업 흐름에 얼마나 잘 맞는지에 달려 있습니다. 이를 위해 우리는 TRIAD 프레임워크를 소개합니다: 신뢰할 수 있는 거버넌스, 실제 임상 가치, 통합 적응형 배포로서 임상 AI의 개발, 검증 및 배포를 안내합니다. TRIAD는 명확한 데이터 출처와 의도된 사용, 공정성 감사 및 보정된 불확실성을 요구합니다. 이 프레임워크는 정확성, 안전성, 작업 부담, 수용, 수정 및 재정의 패턴을 포함한 팀 수준 지표를 사용하여 실제 작업 흐름에서 인간-AI 팀을 평가합니다. 배포는 사전 등록된 가드레일과 지속적인 성능 및 하위 그룹 영향 모니터링을 통한 단계적 롤아웃으로 진행됩니다. TRIAD는 지능을 AI 모델 단독이 아닌 인간-AI 팀의 특성으로 봅니다. 임상의와 환자를 중심으로 거버넌스, 평가 및 배포를 조정함으로써 안전성, 공평성, 효율성 및 경험에서 지속 가능한 향상을 가능하게 하여 임상 가치를 높입니다.

키워드

TRIAD;인공지능(AI);인간-AI 협업;신뢰 메커니즘;임상 의사결정 지원

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