Use of artificial intelligence technologies in laboratory medicine, their effectiveness and application scenarios: a systematic review

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Abstract

BACKGROUND: With the increasing volume of data, laboratory medicine requires automation and standardization of routine processes to reduce workload on healthcare professionals and clear their time for more specialized tasks. Machine learning models and artificial neural networks support image recognition and analysis of large data sets, which allows their integration into laboratory workflows to solve routine tasks.

AIM: This study aimed to analyze global scientific publications on the application of artificial intelligence technologies in laboratory medicine and their potential to address current challenges and identify barriers in their integration into laboratory workflows.

METHODS: A search for publications was conducted using PubMed, manufacturer websites offering ready-to-use laboratory solutions, and reference lists from other reviews. The Mendeley software was utilized for bibliographic data management. The search covered the time interval 2019–2024. Obtained data included bibliometric indicators, research areas, key methodological characteristics, diagnostic effectiveness values for artificial intelligence systems and healthcare professionals, the number and experience of involved healthcare professionals, and validated outcomes of artificial intelligence implementation. The study quality was assessed using a modified QUADAS-CAD checklist.

RESULTS: Twenty-three publications presenting studies at the pre-analytical (n = 1), analytical (n = 19), and post-analytical (n = 3) stages of laboratory analysis were included. Most studies focused on cytology and microbiology, accounting for 48% and 35% of the studies, respectively. Artificial intelligence demonstrated high effectiveness in solving tasks across all stages of the laboratory process. Moreover, its diagnostic accuracy was comparable to that of healthcare professionals; however, decision-making speed was higher. All studies demonstrated a risk of systematic bias, which was associated with unbalanced samples, lacking external data validation, and incomplete description of datasets and analytical methods.

CONCLUSION: Artificial intelligence demonstrates high potential in diagnostic accuracy and processing speed, making it a promising tool to be integrated into laboratory practice and automation of routine processes. However, to achieve this, research methodologies for artificial intelligence should be standardized to reduce the risk of systematic bias, establish reference values for laboratories to ensure the reproducibility and generalizability of results, raise awareness among healthcare professionals and patients on how artificial intelligence works to overcome prejudices, and develop reliable mechanisms for protecting personal data when using artificial intelligence.

About the authors

Yuriy A. Vasilev

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: npcmr@zdrav.mos.ru
ORCID iD: 0000-0002-5283-5961
SPIN-code: 4458-5608

MD, Cand. Sci. (Medicine)

Russian Federation, 24 Petrovka st, bldg 1, Moscow, 127051

Olga G. Nanova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Author for correspondence.
Email: nanova@mail.ru
ORCID iD: 0000-0001-8886-3684
SPIN-code: 6135-4872

Cand. Sci. (Biology)

Russian Federation, 24 Petrovka st, bldg 1, Moscow, 127051

Anton V. Vladzymyrskyy

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: VladzimirskijAV@zdrav.mos.ru
ORCID iD: 0000-0002-2990-7736
SPIN-code: 3602-7120

MD, Dr. Sci. (Medicine)

Russian Federation, 24 Petrovka st, bldg 1, Moscow, 127051

Arcadiy S. Goldberg

The Russian Medical Academy of Continuous Professional Education

Email: goldarcadiy@gmail.com
ORCID iD: 0000-0002-2787-4731
SPIN-code: 8854-0469

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Ivan A. Blokhin

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: BlokhinIA@zdrav.mos.ru
ORCID iD: 0000-0002-2681-9378
SPIN-code: 3306-1387

MD, Cand. Sci. (Medicine)

Russian Federation, 24 Petrovka st, bldg 1, Moscow, 127051

Roman V. Reshetnikov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: ReshetnikovRV1@zdrav.mos.ru
ORCID iD: 0000-0002-9661-0254
SPIN-code: 8592-0558

Cand. Sci. (Physics and Mathematics)

Russian Federation, 24 Petrovka st, bldg 1, Moscow, 127051

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Supplementary files

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1. JATS XML
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2. Supplement 1. List of publications included in the systematic review and their characteristics
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3. Supplement 2. List of publications excluded from the systematic review
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4. Supplement 3. Main characteristics of the studies presented in the publications included in the systematic review 10.17816/DD635349-4334770
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5. Supplement 4. Characteristics of samples and machine learning models used, or commercially available solutions presented in the studies
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6. Supplement 5. Effectiveness of artificial intelligence in studies
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7. Supplement 6. Comparative analysis of the diagnostic efficiency of artificial intelligence and medical workers
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8. Supplement 7. Assessment of the quality of research methodology using the modified QUADAS-CAD questionnaire
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9. Fig. 1. Assessment of the risk of systematic error using the modified QUADAS-CAD questionnaire. QUADAS-CAD (Quality Assessment of Diagnostic Accuracy Studies Computer-Aided Detection) is a specialized modified questionnaire for assessing the risk of systematic errors and the applicability of research in the field of artificial intelligence technologies.

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