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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Artificial intelligence in the clinical laboratory.

Hanjing Hou1, Rui Zhang1, Jinming Li1

  • 1National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.

Clinica Chimica Acta; International Journal of Clinical Chemistry
|May 11, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers significant potential to optimize laboratory medicine workflows. However, current AI models face challenges like cost and accuracy, necessitating further research and development for widespread clinical adoption.

Keywords:
Artificial intelligenceClinical laboratoryLaboratory medicineMachine learning

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Area of Science:

  • Clinical laboratory science
  • Medical artificial intelligence

Background:

  • Laboratory medicine is increasingly automated.
  • Artificial intelligence (AI) is gaining traction for optimizing laboratory workflows.
  • Limited approved AI models exist, with drawbacks like cost and accuracy.

Purpose of the Study:

  • To review the current status of AI in laboratory medicine.
  • To identify challenges and solutions for AI implementation.
  • To explore future opportunities for AI in clinical testing.

Main Methods:

  • Literature review of AI applications in laboratory medicine.
  • Analysis of AI models in clinical use and emerging studies.
  • Discussion of challenges, solutions, and future prospects.

Main Results:

  • AI can optimize laboratory workflows and potentially revolutionize the field.
  • Current AI models have limitations including cost, accuracy, and need for manual review.
  • The review covers AI applications across various stages of clinical testing.

Conclusions:

  • AI holds promise for advancing laboratory medicine.
  • Addressing current challenges is crucial for broader AI adoption.
  • Future research should focus on improving AI accuracy, cost-effectiveness, and integration.