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000212479 0247_ $$2doi$$a10.1515/cclm-2022-1151
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000212479 037__ $$aDKFZ-2023-00187
000212479 041__ $$aEnglish
000212479 082__ $$a610
000212479 1001_ $$aLennerz, Jochen K$$b0
000212479 245__ $$aDiagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML.
000212479 260__ $$aBerlin [u.a.]$$bDe Gruyter$$c2023
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000212479 500__ $$aDepartment of Otorhinolaryngology, Head and Neck Surgery, German Cancer Research Center (DKFZ), / 2023 Jan 25;61(4):544-557
000212479 520__ $$aLaboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing.A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on 'AI in the Laboratory of the Future' prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations.The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems.A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.
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000212479 650_7 $$2Other$$aartificial intelligence
000212479 650_7 $$2Other$$abiomarker
000212479 650_7 $$2Other$$alaboratory medicine
000212479 650_7 $$2Other$$amachine learning
000212479 650_7 $$2Other$$aregulatory science
000212479 7001_ $$aSalgado, Roberto$$b1
000212479 7001_ $$aKim, Grace E$$b2
000212479 7001_ $$aSirintrapun, Sahussapont Joseph$$b3
000212479 7001_ $$0P:(DE-HGF)0$$aThierauf, Julia C$$b4
000212479 7001_ $$aSingh, Ankit$$b5
000212479 7001_ $$aIndave, Iciar$$b6
000212479 7001_ $$aBard, Adam$$b7
000212479 7001_ $$aWeissinger, Stephanie E$$b8
000212479 7001_ $$aHeher, Yael K$$b9
000212479 7001_ $$ade Baca, Monica E$$b10
000212479 7001_ $$aCree, Ian A$$b11
000212479 7001_ $$aBennett, Shannon$$b12
000212479 7001_ $$aCarobene, Anna$$b13
000212479 7001_ $$aOzben, Tomris$$b14
000212479 7001_ $$aRitterhouse, Lauren L$$b15
000212479 773__ $$0PERI:(DE-600)1492732-9$$a10.1515/cclm-2022-1151$$gVol. 0, no. 0$$n4$$p544-557$$tClinical chemistry and laboratory medicine$$v61$$x1434-6621$$y2023
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