用于评估实验室医学中人工智能/机器学习研究的EFLM核对表:加强用于实验室特定应用的一般医学人工智能框架。

IF 3.7 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY
Anna Carobene, Janne Cadamuro, Glynis Frans, Hanoch Goldshmidt, Zeljiko Debeljak, Sander De Bruyne, William van Doorn, Johannes Elias, Habib Özdemir, Salomon Martin Perez, Helena Lame, Alexander Tolios, Federico Cabitza, Andrea Padoan
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引用次数: 0

摘要

将人工智能(AI)和机器学习(ML)整合到实验室医学中,有望推动诊断、预后和决策支持工具的发展;然而,常规临床实施仍然是有限的和异质性的。实验室数据呈现出独特的方法和语义复杂性——方法依赖性、分析物特异性变异和上下文敏感性——通用人工智能报告指南没有充分解决这些问题。为了弥补这一差距,EFLM数字化和人工智能委员会(C-AI)提出了一份扩展的清单,以支持评估基于实验室数据开发AI/ML模型的需求和建议。在广泛采用的ChAMAI清单(医疗人工智能评估清单)的基础上,我们的提案引入了六个额外的项目,每个项目都以跨行业数据挖掘标准流程(CRISP-DM)框架为基础,并根据实验室工作流程的特殊性进行了定制。这些扩展涉及:(1)明确记录实验室数据特征;(2)考虑生物和分析的可变性;(3)元数据和数据在情境化结果中的作用;(4)分析物协调和标准化实践;(5)严格的外部验证,注重数据集的相似性;(6)实施公平数据原则,以提高透明度和可重复性。总之,这些建议旨在培养适合在临床实验室环境中部署的健壮、可解释和可推广的人工智能系统。通过将这些实验室意识的考虑纳入模型开发管道,研究人员和实践者可以增强人工智能工具的科学严谨性和实际适用性。我们提倡开发人员、审稿人和监管机构采用这一扩展清单,以促进实验室医学中可信赖和可重复的人工智能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EFLM checklist for the assessment of AI/ML studies in laboratory medicine: enhancing general medical AI frameworks for laboratory-specific applications.

The integration of artificial intelligence (AI) and machine learning (ML) into laboratory medicine shows promise for advancing diagnostic, prognostic, and decision-support tools; however, routine clinical implementation remains limited and heterogeneous. Laboratory data presents unique methodological and semantic complexities - method dependency, analyte-specific variation, and contextual sensitivity-not adequately addressed by general-purpose AI reporting guidelines. To bridge this gap, the EFLM Committee on Digitalisation and Artificial Intelligence (C-AI) proposes an expanded checklist to support assessment of requirements and recommendations for the development of AI/ML models based on laboratory data. Building upon the widely adopted ChAMAI checklist (Checklist for assessment of medical AI), our proposal introduces six additional items, each grounded in the CRoss Industry Standard Process for Data Mining (CRISP-DM) framework and tailored to the specificities of laboratory workflows. These extensions address: (1) explicit documentation of laboratory data characteristics; (2) consideration of biological and analytical variability; (3) the role of metadata and peridata in contextualizing results; (4) analyte harmonization and standardization practices; (5) rigorous external validation with attention to dataset similarity; and (6) the implementation of FAIR data principles for transparency and reproducibility. Together, these recommendations aim to foster robust, interpretable, and generalizable AI systems that are fit for deployment in clinical laboratory settings. By incorporating these laboratory-aware considerations into model development pipelines, researchers and practitioners can enhance both the scientific rigor and practical applicability of AI tools. We advocate for the adoption of this extended checklist by developers, reviewers, and regulators to promote trustworthy and reproducible AI in laboratory medicine.

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来源期刊
Clinical chemistry and laboratory medicine
Clinical chemistry and laboratory medicine 医学-医学实验技术
CiteScore
11.30
自引率
16.20%
发文量
306
审稿时长
3 months
期刊介绍: Clinical Chemistry and Laboratory Medicine (CCLM) publishes articles on novel teaching and training methods applicable to laboratory medicine. CCLM welcomes contributions on the progress in fundamental and applied research and cutting-edge clinical laboratory medicine. It is one of the leading journals in the field, with an impact factor over 3. CCLM is issued monthly, and it is published in print and electronically. CCLM is the official journal of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) and publishes regularly EFLM recommendations and news. CCLM is the official journal of the National Societies from Austria (ÖGLMKC); Belgium (RBSLM); Germany (DGKL); Hungary (MLDT); Ireland (ACBI); Italy (SIBioC); Portugal (SPML); and Slovenia (SZKK); and it is affiliated to AACB (Australia) and SFBC (France). Topics: - clinical biochemistry - clinical genomics and molecular biology - clinical haematology and coagulation - clinical immunology and autoimmunity - clinical microbiology - drug monitoring and analysis - evaluation of diagnostic biomarkers - disease-oriented topics (cardiovascular disease, cancer diagnostics, diabetes) - new reagents, instrumentation and technologies - new methodologies - reference materials and methods - reference values and decision limits - quality and safety in laboratory medicine - translational laboratory medicine - clinical metrology Follow @cclm_degruyter on Twitter!
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