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Cited by 42Artificial Intelligence in Healthcare and EducationClinical Laboratory Practices and Quality ControlArtificial Intelligence in Healthcare

Flowing through laboratory clinical data: the role of artificial intelligence and big data

Open access
Padoan, Andrea, Plebani, Mario·2022·Clinical Chemistry and Laboratory Medicine (CCLM)
DOI 10.1515/cclm-2022-0653
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Abstract

During the last few years, clinical laboratories have faced a sea change, from facilities producing a high volume of low-cost test results, toward a more integrated and patient-centered service. Parallel to this paradigm change, the digitalization of healthcare data has made an enormous quantity of patients’ data easily accessible, thus opening new scenarios for the utilization of artificial intelligence (AI) tools. Every day, clinical laboratories produce a huge amount of information, of which patients’ results are only a part. The laboratory information system (LIS) may include other “relevant” compounding data, such as internal quality control or external quality assessment (EQA) results, as well as, for example, timing of test requests and of blood collection and exams transmission, these data having peculiar characteristics typical of big data, as volume, velocity, variety, and veracity, potentially being used to generate value in patients’ care. Despite the increasing interest expressed in AI and big data in laboratory medicine, these topics are approaching the discipline slowly for several reasons, attributable to lack of knowledge and skills but also to poor or absent standardization, harmonization and problematic regulatory and ethical issues. Finally, it is important to bear in mind that the mathematical postulation of algorithms is not sufficient for obtaining useful clinical tools, especially when biological parameters are not evaluated in the appropriate context. It is therefore necessary to enhance cooperation between laboratory and AI experts, and to coordinate and govern processes, thus favoring the development of valuable clinical tools.

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  • In clinical laboratories, a significant amount of information is generated daily throughout the entire testing process.

    “In clinical laboratories, in addition to test results, a signi fi cant amount of information is generated daily throughout the entire testing process [60].”

    From Point-of-care testing: state-of-the art and perspectives (2025) · cites this paper as [60]

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Authors
Padoan, A., & Plebani, M.
Affiliations
University of Padua (IT)
Corresponding author
Padoan, Andrea
ORCID
Venue
Clinical Chemistry and Laboratory Medicine (CCLM)
Publisher
Walter de Gruyter GmbH
Published
2022-07-18
Volume
60
Issue
12
Pages
1875-1880
ISSN
  • 1434-6621 (ISSN-L)
  • 1437-4331
DOI
10.1515/cclm-2022-0653
OpenAlex
W4285726173
Cited by
42
References
31
Citations by year
2026: 7 · 2025: 9 · 2024: 11 · 2023: 11 · 2022: 4
FWCI
2.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Clinical Chemistry and Laboratory Medicine (CCLM) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Padua Research Archive (University of Padova) (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Physiology › Clinical Laboratory Practices and Quality Control
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
artificial intelligence, big data, clinical laboratory, laboratory information systems, laboratory findings, quality control data, external quality assessment, clinical decision support, standardization
MeSH terms
Big Data, Algorithms, Artificial Intelligence, Delivery of Health Care, Humans, Knowledge

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