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- Title
Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML.
- Authors
Lennerz, Jochen K.; Salgado, Roberto; Kim, Grace E.; Sirintrapun, Sahussapont Joseph; Thierauf, Julia C.; Singh, Ankit; Indave, Iciar; Bard, Adam; Weissinger, Stephanie E.; Heher, Yael K.; de Baca, Monica E.; Cree, Ian A.; Bennett, Shannon; Carobene, Anna; Ozben, Tomris; Ritterhouse, Lauren L.
- Abstract
Laboratory 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.
- Subjects
ARTIFICIAL intelligence; MACHINE learning; EUROPEAN integration; TOTAL quality management; OPERATIONAL definitions
- Publication
Clinical Chemistry & Laboratory Medicine, 2023, Vol 61, Issue 4, p544
- ISSN
1434-6621
- Publication type
Article
- DOI
10.1515/cclm-2022-1151