Establishing a comprehensive artificial intelligence lifecycle framework for laboratory medicine and pathology: A series introduction.

Journal: American journal of clinical pathology
Published Date: Jul 12, 2025

Abstract

OBJECTIVE: Despite exponential growth in artificial intelligence (AI) research for laboratory medicine and pathology, a significant gap exists between model development and clinical AI implementation. This article introduces a structured framework, the Clinical AI Readiness Evaluator (CARE), to bridge this gap and support the responsible adoption of AI in clinical laboratory settings.

Authors

  • Christopher A Garcia
    Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st St. SW, Rochester, MN, 55901, USA.
  • Katelyn A Reed
    Department of Laboratory Medicine and Pathology, Division of Computational Pathology and Informatics, Mayo Clinic, Rochester, MN, United States.
  • Eric Lantz
    Department of Laboratory Medicine and Pathology, Division of Computational Pathology and Informatics, Mayo Clinic, Rochester, MN, United States.
  • Patrick Day
    Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
  • Mark D Zarella
  • Steven N Hart
    From the Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, Minnesota (Flotte, Derauf, Byrd, Kroneman, Bell, Hart, Garcia).
  • Eric Will
    Center for Digital Health, Mayo Clinic, Rochester, MN, United States.
  • John G Skiffington
    Center for Digital Health, Mayo Clinic, Rochester, MN, United States.
  • Melinda Rice
    Center for Digital Health, Mayo Clinic, Rochester, MN, United States.
  • Debra A Novak
    Department of Laboratory Medicine and Pathology, Division of Computational Pathology and Informatics, Mayo Clinic, Rochester, MN, United States.
  • David S McClintock
    Department of Pathology, University of Michigan, Ann Arbor, Michigan.

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