Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Jan 25;61(4):544-557.
doi: 10.1515/cclm-2022-1151. Print 2023 Mar 28.

Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML

Jochen K Lennerz  1 , Roberto Salgado  2   3 , Grace E Kim  4 , Sahussapont Joseph Sirintrapun  5 , Julia C Thierauf  1   6 , Ankit Singh  1 , Iciar Indave  7 , Adam Bard  1 , Stephanie E Weissinger  8 , Yael K Heher  1 , Monica E de Baca  9 , Ian A Cree  10 , Shannon Bennett  11 , Anna Carobene  12 , Tomris Ozben  13   14 , Lauren L Ritterhouse  1
Affiliations
Free article

Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML

Jochen K Lennerz et al. Clin Chem Lab Med. .
Free article

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: artificial intelligence; biomarker; laboratory medicine; machine learning; regulatory science.

PubMed Disclaimer

References

    1. Naylor, S, Chen, JY. Unraveling human complexity and disease with systems biology and personalized medicine. Per Med 2010;7:275–89. https://doi.org/10.2217/pme.10.16 . - DOI
    1. Sahu, M, Gupta, R, Ambasta, RK, Kumar, P. Artificial intelligence and machine learning in precision medicine: a paradigm shift in big data analysis. Prog Mol Biol Transl Sci 2022;190:57–100. https://doi.org/10.1016/bs.pmbts.2022.03.002 . - DOI
    1. Padoan, A, Plebani, M. Flowing through laboratory clinical data: the role of artificial intelligence and big data. Clin Chem Lab Med 2022;60:1875–80. https://doi.org/10.1515/cclm-2022-0653 . - DOI
    1. Khan, ZUN, Jafri, L, Hall, PL, Schultz, MJ, Ahmed, S, Khan, AH, et al.. Utilizing augmented artificial intelligence for aminoacidopathies using collaborative laboratory integrated reporting- a cross-sectional study. Ann Med Surg 2022;82:104651. https://doi.org/10.1016/j.amsu.2022.104651 . - DOI
    1. Constantinescu, G, Schulze, M, Peitzsch, M, Hofmockel, T, Scholl, UI, Williams, TA, et al.. Integration of artificial intelligence and plasma steroidomics with laboratory information management systems: application to primary aldosteronism. Clin Chem Lab Med 2022;60:1929–37. https://doi.org/10.1515/cclm-2022-0470 . - DOI

LinkOut - more resources