Preparing the Future Laboratory Workforce for Intelligent Diagnostics: The Artificial Intelligence Competency Framework for Laboratory Medicine Education (AILab-CF)
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Abstract
Background: Artificial intelligence (AI) is rapidly transforming laboratory medicine by integrating it into diagnostic interpretation, digital pathology, molecular diagnostics, laboratory automation, quality management systems, and predictive analytics. While AI competency frameworks have emerged across health professions education, most existing models have been developed from physician-centered or general healthcare perspectives and provide limited guidance regarding the specific educational requirements of laboratory medicine professionals. As intelligent technologies become increasingly embedded within diagnostic workflows, the absence of a discipline-specific competency framework represents an important educational gap with implications for workforce preparedness, professional accountability, and responsible AI implementation.
Objective: To develop a competency-based educational framework specifically designed to prepare current and future laboratory medicine professionals for safe, effective, ethical, and responsible practice within AI-enabled diagnostic environments.
Methods: A conceptual framework-development approach was used. The framework was constructed through an iterative critical synthesis of literature and established concepts in artificial intelligence, laboratory medicine, digital health, health professions education, competency-based education, data governance, and responsible AI. The work was not conducted as a systematic review, and no retrospective systematic-search procedure is claimed. Existing frameworks and guidance documents already identified during framework development were compared for scope, target population, competency emphasis, developmental structure, and relevance to laboratory practice; recurring concepts and laboratory-specific gaps were then translated into candidate principles, competency domains, developmental descriptors, and implementation elements, followed by iterative refinement for conceptual coherence and practical applicability.
Results: The Artificial Intelligence Competency Framework for Laboratory Medicine Education (AILab-CF) was developed as a comprehensive competency architecture consisting of six interdependent domains: (1) AI Foundations, (2) Data and Digital Literacy, (3) AI-Assisted Diagnostic Interpretation, (4) Ethics, Governance, and Responsible AI, (5) Human–AI Collaboration, and (6) Innovation, Leadership and Lifelong Learning. The framework is supported by five foundational educational principles and incorporates a four-level developmental continuum comprising awareness, application, advanced practice, and leadership. In addition, a longitudinal implementation model was developed to facilitate integration across undergraduate education, internship training, postgraduate education, continuing professional development, and leadership pathways. Collectively, these components provide a structured approach to curriculum development, competency assessment, faculty development, workforce planning, and lifelong learning within AI-enabled laboratory medicine.
Discussion: AILab-CF addresses an important gap in health professions education by offering a laboratory medicine-specific competency architecture for AI-enabled diagnostic practice. In contrast to predominantly physician-oriented, healthcare-wide, or general digital-health frameworks, AILab-CF explicitly connects laboratory data stewardship, diagnostic validation and interpretation, human oversight, responsible governance, interdisciplinary collaboration, and lifelong professional adaptation. The framework is proposed as a conceptual model requiring future expert consensus and empirical validation.
Conclusions: The transition toward AI-enabled laboratory medicine requires professionals who can combine technical competence with critical judgment, ethical accountability, and continuous adaptation. AILab-CF provides a structured educational foundation to prepare the future laboratory workforce for intelligent diagnostics and may serve as a guide for curriculum reform, workforce development, and the responsible implementation of AI technologies across laboratory medicine.
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