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Cited by 14Artificial Intelligence in Healthcare and EducationClinical Laboratory Practices and Quality ControlArtificial Intelligence in Healthcare

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How laboratory medicine will change in the near future: integrating artificial intelligence, automation, and human expertise in the era of Industry 5.0

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Giesriegl, Florian, Mrazek, Cornelia, Cadamuro, Janne·2025·Journal of Laboratory and Precision Medicine
DOI 10.21037/jlpm-25-6
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Abstract

The integration of artificial intelligence (AI) and machine learning (ML) into medical diagnostic processes will soon disrupt the way laboratories and its specialists are working today. In this review we explore the transformation from manual to assisted or automated labor, starting from the first industrial revolution to the upcoming human-centered Industry 5.0. This review aims to provide a holistic overview of the present and possible future of AI assistance in laboratory diagnostics. It explores opportunities for AI in laboratory medicine, not only as a standalone discipline, but also in a broader diagnostic context, such as integrated diagnostics and AI-assisted diagnostic demand management. It addresses implementation challenges, including data quality, regulatory and ethical considerations, the necessity of investments and skilled workforce and the crucial aspect of building trust towards AI-assisted patient care. The review also addresses potential pitfalls, such as ML models failing to acknowledge medical context and the risk of innovation shortages due to the potential lack of learning from errors, when relying on AI. Furthermore, the knowledge and attitude towards AI among laboratory professionals are examined, alongside examples of current AI applications in the laboratory. The review delves into the value of AI in the context of the emerging topic of patient engagement in healthcare and it discusses the risk of replacement of healthcare professionals by this new technology. Finally, the review proposes a suggested roadmap for successful AI integration in laboratory medicine, while emphasizing the importance of multidisciplinary collaboration and change management. In conclusion, we need to embrace these new technologies, firstly because they will massively improve patient care, freeing us from mundane and dull tasks and allowing us to focus more on complex cases, which may currently be overlooked within the sheer avalanche of laboratory data and secondly because this disruptive evolution is inevitable.

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  • Acceptance of AI and deep learning models by regulatory authorities is hampered by lack of clarity in their performance, as uninterpretable predictions do not provide a clear link to biological processes and specific safety characteristics of strains.

    “Although AI and deep learning models have potential in probiotic research, their acceptance by regulatory authorities is hampered by the lack of clarity in their performance, as uninterpretable predictions do not provide a clear link to biological processes and specific safety characteristics of strains [167,170].”

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Metadata

Authors
Giesriegl, F., Mrazek, C., & Cadamuro, J.
Affiliations
Paracelsus Medical University (AT)
ORCID
Cadamuro, Janne
Venue
Journal of Laboratory and Precision Medicine
Publisher
AME Publishing Company
Published
2025-07
Volume
10
Pages
12-12
ISSN
2519-9005 (ISSN-L)
DOI
10.21037/jlpm-25-6
OpenAlex
W4412769232
Cited by
13
References
0
Citations by year
2026: 12 · 2025: 1
FWCI
3.16
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
Journal of Laboratory and Precision Medicine (publishedVersion, cc-by-nc-nd)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Medical Laboratory Technology › Clinical Laboratory Practices and Quality Control
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
clinical pathology, artificial intelligence, laboratory automation, human expertise, Industry 5.0, human-AI collaboration

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